

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					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

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)

@is_pt_tf_cross_test
class 			a					(		unittest.TestCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						Optional[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Any							=      TFAutoModel.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      AutoModel.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						List[str]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      TFAutoModelForPreTraining.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[str]							=      AutoModelForPreTraining.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForCausalLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      TFAutoModelForCausalLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[int]							=      AutoModelForCausalLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						str							=      AutoModelForCausalLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[int]							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						int							=      TFAutoModelForMaskedLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForMaskedLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[str]							=      AutoModelForMaskedLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      AutoModelForMaskedLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Tuple							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      TFAutoModelForSeqaSeqLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForSeqaSeqLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      AutoModelForSeqaSeqLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      AutoModelForSeqaSeqLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						Optional[int]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      TFAutoModelForSequenceClassification.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Tuple							=      AutoModelForSequenceClassification.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						int							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Any							=      TFAutoModelForQuestionAnswering.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Dict							=      AutoModelForQuestionAnswering.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)

										_UpperCAmelCase    :						Optional[Any]							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)

										_UpperCAmelCase    :						List[str]							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


