import pytest  # used for our unit tests
from openai._models import _extract_field_schema_pv2


# Minimal stand-ins for pydantic_core.core_schema structures
class ModelField(dict): pass

# ------------------ UNIT TESTS ------------------

# Basic Test Cases


















#------------------------------------------------
from typing import Any

# imports
import pytest
from openai._models import _extract_field_schema_pv2


class BaseModel:
    # Dummy BaseModel for testing purposes
    __pydantic_core_schema__: dict
from openai._models import _extract_field_schema_pv2


# Helper to create a dummy model class with a given schema
def make_model(fields: dict, model_type="model-fields", top_type="model"):
    class DummyModel(BaseModel):
        pass
    schema = {
        "type": top_type,
        "schema": {
            "type": model_type,
            "fields": fields
        }
    }
    DummyModel.__pydantic_core_schema__ = schema
    return DummyModel

# --------------------------
# 1. Basic Test Cases
# --------------------------





def test_edge_top_schema_not_model():
    # Edge: top-level schema type is not 'model'
    fields = {"y": {"type": "str"}}
    model = make_model(fields, top_type="not-model")
    codeflash_output = _extract_field_schema_pv2(model, "y"); result = codeflash_output # 928ns -> 1.01μs (7.75% slower)

def test_edge_fields_schema_not_model_fields():
    # Edge: fields schema type is not 'model-fields'
    fields = {"z": {"type": "float"}}
    model = make_model(fields, model_type="not-model-fields")
    codeflash_output = _extract_field_schema_pv2(model, "z"); result = codeflash_output # 957ns -> 728ns (31.5% faster)

def test_edge_empty_fields():
    # Edge: fields dict is empty
    fields = {}
    model = make_model(fields)
    codeflash_output = _extract_field_schema_pv2(model, "anything"); result = codeflash_output # 1.20μs -> 944ns (26.7% faster)

def test_edge_field_is_none():
    # Edge: field value is None (should not happen, but test anyway)
    fields = {"foo": None}
    model = make_model(fields)
    codeflash_output = _extract_field_schema_pv2(model, "foo"); result = codeflash_output # 1.17μs -> 889ns (31.2% faster)

def test_edge_field_name_is_empty_string():
    # Edge: field name is empty string
    fields = {"": {"type": "empty"}}
    model = make_model(fields)
    codeflash_output = _extract_field_schema_pv2(model, ""); result = codeflash_output # 1.27μs -> 1.09μs (16.8% faster)

def test_edge_field_name_is_non_string():
    # Edge: field name is not a string (should not happen, but test anyway)
    fields = {123: {"type": "int"}}
    model = make_model(fields)
    codeflash_output = _extract_field_schema_pv2(model, 123); result = codeflash_output # 1.33μs -> 1.11μs (19.3% faster)

def test_edge_fields_dict_is_missing():
    # Edge: fields dict is missing from schema
    class DummyModel(BaseModel):
        pass
    DummyModel.__pydantic_core_schema__ = {
        "type": "model",
        "schema": {
            "type": "model-fields"
            # 'fields' key missing
        }
    }
    with pytest.raises(KeyError):
        _extract_field_schema_pv2(DummyModel, "foo") # 1.47μs -> 1.16μs (26.1% faster)


def test_large_scale_many_fields():
    # Large scale: model with 1000 fields
    fields = {f"field_{i}": {"type": "int", "default": i} for i in range(1000)}
    model = make_model(fields)
    # Test random selection of fields
    for i in range(0, 1000, 100):
        name = f"field_{i}"
        codeflash_output = _extract_field_schema_pv2(model, name); result = codeflash_output # 6.27μs -> 5.28μs (18.7% faster)
    # Test for a field that does not exist
    codeflash_output = _extract_field_schema_pv2(model, "field_1001") # 560ns -> 421ns (33.0% faster)

def test_large_scale_fields_are_large_objects():
    # Large scale: fields dict with large objects as values
    fields = {f"f{i}": {"type": "str", "data": "x" * 100} for i in range(500)}
    model = make_model(fields)
    # Check retrieval for a sample
    for i in range(0, 500, 50):
        name = f"f{i}"
        codeflash_output = _extract_field_schema_pv2(model, name); result = codeflash_output # 6.04μs -> 4.82μs (25.5% faster)

def test_large_scale_field_name_length():
    # Large scale: field names are very long strings
    long_name = "a" * 500
    fields = {long_name: {"type": "str"}}
    model = make_model(fields)
    codeflash_output = _extract_field_schema_pv2(model, long_name); result = codeflash_output # 1.18μs -> 1.03μs (14.9% faster)

def test_large_scale_all_fields_none():
    # Large scale: all field values are None
    fields = {f"f{i}": None for i in range(100)}
    model = make_model(fields)
    for i in range(100):
        codeflash_output = _extract_field_schema_pv2(model, f"f{i}") # 42.7μs -> 30.8μs (38.5% faster)

def test_large_scale_field_value_is_dict_with_nested_dict():
    # Large scale: field values are nested dicts
    fields = {f"f{i}": {"type": "dict", "nested": {"a": i}} for i in range(100)}
    model = make_model(fields)
    for i in range(0, 100, 10):
        name = f"f{i}"
        codeflash_output = _extract_field_schema_pv2(model, name); result = codeflash_output # 5.55μs -> 4.56μs (21.9% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.