




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
		from transformers import (
		    TFAutoModel,
		    TFAutoModelForCausalLM,
		    TFAutoModelForMaskedLM,
		    TFAutoModelForPreTraining,
		    TFAutoModelForQuestionAnswering,
		    TFAutoModelForSeqaSeqLM,
		    TFAutoModelForSequenceClassification,
		    TFAutoModelForTableQuestionAnswering,
		    TFAutoModelForTokenClassification,
		    TFAutoModelWithLMHead,
		    TFBertForMaskedLM,
		    TFBertForPreTraining,
		    TFBertForQuestionAnswering,
		    TFBertForSequenceClassification,
		    TFBertModel,
		    TFFunnelBaseModel,
		    TFFunnelModel,
		    TFGPTaLMHeadModel,
		    TFRobertaForMaskedLM,
		    TFTaForConditionalGeneration,
		    TFTapasForQuestionAnswering,
		)
		from transformers.models.auto.modeling_tf_auto import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TF_MODEL_MAPPING,
		)
		from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
		from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''new-model'''


if is_tf_available():




		class 			UpperCAmelCase_      (   A       ):




									'''simple docstring'''



									a__						  =     NewModelConfig




@require_tf
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					"""bert-base-cased"""
										SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					"""bert-base-cased"""
										SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					TFAutoModelForPreTraining.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Any:
										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForCausalLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForCausalLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForMaskedLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForMaskedLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForSeqaSeqLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForSeqaSeqLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Dict			)			->					Any:
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForSequenceClassification.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForQuestionAnswering.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							@require_tensorflow_probability
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForTableQuestionAnswering.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForTableQuestionAnswering.from_pretrained(
													    a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
										self.assertIsInstance(a       ,     a			)
										self.assertEqual(model.num_parameters()       ,     14_410			)
										self.assertEqual(model.num_parameters(only_trainable=a			)       ,     14_410			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
										self.assertIsInstance(a       ,     a			)
										self.assertEqual(model.num_parameters()       ,     14_410			)
										self.assertEqual(model.num_parameters(only_trainable=a			)       ,     14_410			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Optional[int]:
										# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""sgugger/funnel-random-tiny"""			)
										self.assertIsInstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					copy.deepcopy(model.config			)
										SCREAMING_SNAKE_CASE           =					["""FunnelBaseModel"""]
										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_config(a			)
										self.assertIsInstance(a       ,     a			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													model.save_pretrained(a			)
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a			)
													self.assertIsInstance(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										try:
													AutoConfig.register("""new-model"""       ,     a			)

													SCREAMING_SNAKE_CASE           =					[
													    TFAutoModel,
													    TFAutoModelForCausalLM,
													    TFAutoModelForMaskedLM,
													    TFAutoModelForPreTraining,
													    TFAutoModelForQuestionAnswering,
													    TFAutoModelForSequenceClassification,
													    TFAutoModelForTokenClassification,
													]

													for auto_class in auto_classes:
																with self.subTest(auto_class.__name__			):
																			# Wrong config class will raise an error
																			with self.assertRaises(a			):
																						auto_class.register(a       ,     a			)
																			auto_class.register(a       ,     a			)
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(a			):
																						auto_class.register(a       ,     a			)

																			# Now that the config is registered, it can be used as any other config with the auto-API
																			SCREAMING_SNAKE_CASE           =					BertModelTester(self			).get_config()
																			SCREAMING_SNAKE_CASE           =					NewModelConfig(**tiny_config.to_dict()			)
																			SCREAMING_SNAKE_CASE           =					auto_class.from_config(a			)
																			self.assertIsInstance(a       ,     a			)

																			with tempfile.TemporaryDirectory() as tmp_dir:
																						model.save_pretrained(a			)
																						SCREAMING_SNAKE_CASE           =					auto_class.from_pretrained(a			)
																						self.assertIsInstance(a       ,     a			)

										finally:
													if "new-model" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["new-model"]
													for mapping in (
													    TF_MODEL_MAPPING,
													    TF_MODEL_FOR_PRETRAINING_MAPPING,
													    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
													    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
													    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
													    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
													    TF_MODEL_FOR_MASKED_LM_MAPPING,
													):
																if NewModelConfig in mapping._extra_content:
																			del mapping._extra_content[NewModelConfig]





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Union[str, Any]:
										with self.assertRaisesRegex(
										    a       ,     """bert-base is not a local folder and is not a valid model identifier"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""bert-base"""			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Union[str, Any]:
										with self.assertRaisesRegex(
										    a       ,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a       ,     revision="""aaaaaa"""			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[Any]:
										with self.assertRaisesRegex(
										    a       ,     """hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"""       ,     ):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Any:
										with self.assertRaisesRegex(a       ,     """Use `from_pt=True` to load this model"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""			)







							def    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										# Make sure we have cached the model.
										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""			)
										with RequestCounter() as counter:
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""			)
													self.assertEqual(counter.get_request_count       ,     0			)
													self.assertEqual(counter.head_request_count       ,     1			)
													self.assertEqual(counter.other_request_count       ,     0			)

										# With a sharded checkpoint
										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""			)
										with RequestCounter() as counter:
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""			)
													self.assertEqual(counter.get_request_count       ,     0			)
													self.assertEqual(counter.head_request_count       ,     1			)
													self.assertEqual(counter.other_request_count       ,     0			)












import os
import sys
import unittest


__A						:							Any							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A						:							List[Any]							=  os.path.join(git_repo_path, """src""", """diffusers""")




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					find_backend("""    if not is_torch_available():"""			)
										self.assertEqual(a       ,     """torch"""			)

										# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
										# self.assertEqual(backend_with_underscore, "tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend("""    if not (is_torch_available() and is_transformers_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers"""			)

										# double_backend_with_underscore = find_backend(
										#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
										# )
										# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend(
										    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers_and_onnx"""			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										SCREAMING_SNAKE_CASE           =					read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn("""torch"""       ,     a			)
										self.assertIn("""torch_and_transformers"""       ,     a			)
										self.assertIn("""flax_and_transformers"""       ,     a			)
										self.assertIn("""torch_and_transformers_and_onnx"""       ,     a			)

										# Likewise, we can't assert on the exact content of a key
										self.assertIn("""UNet2DModel"""       ,     objects["""torch"""]			)
										self.assertIn("""FlaxUNet2DConditionModel"""       ,     objects["""flax"""]			)
										self.assertIn("""StableDiffusionPipeline"""       ,     objects["""torch_and_transformers"""]			)
										self.assertIn("""FlaxStableDiffusionPipeline"""       ,     objects["""flax_and_transformers"""]			)
										self.assertIn("""LMSDiscreteScheduler"""       ,     objects["""torch_and_scipy"""]			)
										self.assertIn("""OnnxStableDiffusionPipeline"""       ,     objects["""torch_and_transformers_and_onnx"""]			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""CONSTANT"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     """\nCONSTANT = None\n"""			)

										SCREAMING_SNAKE_CASE           =					create_dummy_object("""function"""       ,     """'torch'"""			)
										self.assertEqual(
										    a       ,     """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""			)

										SCREAMING_SNAKE_CASE           =					"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""FakeClass"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     a			)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}			)
										self.assertEqual(dummy_files["""torch"""]       ,     a			)






