





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=None ,				_UpperCamelCase=None )		->						Optional[int]:






				"""simple docstring"""


				if attention_mask is None:
								snake_case_						:    Tuple				=  tf.cast(tf.math.not_equal(_UpperCamelCase ,				config.pad_token_id ) ,				tf.inta )
				return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     Optional[Any]       			=      OPTConfig
			lowerCamelCase_     :     Dict       			=      {}
			lowerCamelCase_     :     Union[str, Any]       			=      '''gelu'''






			def __init__(self					,							__magic_name__					,							__magic_name__=13					,							__magic_name__=7					,							__magic_name__=True					,							__magic_name__=False					,							__magic_name__=99					,							__magic_name__=16					,							__magic_name__=2					,							__magic_name__=4					,							__magic_name__=4					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=20					,							__magic_name__=2					,							__magic_name__=1					,							__magic_name__=0					,							__magic_name__=16					,							__magic_name__=16					,							)     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Dict				=  seq_length
							snake_case_						:    Union[str, Any]				=  is_training
							snake_case_						:    Tuple				=  use_labels
							snake_case_						:    List[str]				=  vocab_size
							snake_case_						:    int				=  hidden_size
							snake_case_						:    List[str]				=  num_hidden_layers
							snake_case_						:    List[str]				=  num_attention_heads
							snake_case_						:    Optional[Any]				=  intermediate_size
							snake_case_						:    List[str]				=  hidden_act
							snake_case_						:    int				=  hidden_dropout_prob
							snake_case_						:    Optional[int]				=  attention_probs_dropout_prob
							snake_case_						:    Optional[int]				=  max_position_embeddings
							snake_case_						:    List[str]				=  eos_token_id
							snake_case_						:    Dict				=  pad_token_id
							snake_case_						:    List[Any]				=  bos_token_id
							snake_case_						:    Optional[Any]				=  embed_dim
							snake_case_						:    Dict				=  word_embed_proj_dim
							snake_case_						:    List[str]				=  False






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Any				=  ids_tensor([self.batch_size, self.seq_length - 1]					,							self.vocab_size    )
							snake_case_						:    List[Any]				=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )					,							1    )
							snake_case_						:    Optional[Any]				=  tf.concat([input_ids, eos_tensor]					,							axis=1    )

							snake_case_						:    List[str]				=  self.config_cls(
							    vocab_size=self.vocab_size					,							hidden_size=self.hidden_size					,							num_hidden_layers=self.num_hidden_layers					,							num_attention_heads=self.num_attention_heads					,							ffn_dim=self.intermediate_size					,							dropout=self.hidden_dropout_prob					,							attention_dropout=self.attention_probs_dropout_prob					,							max_position_embeddings=self.max_position_embeddings					,							eos_token_id=self.eos_token_id					,							bos_token_id=self.bos_token_id					,							pad_token_id=self.pad_token_id					,							embed_dim=self.embed_dim					,							word_embed_proj_dim=self.word_embed_proj_dim					,							is_encoder_decoder=__magic_name__					,							**self.config_updates					,							)
							snake_case_						:    Optional[Any]				=  prepare_opt_inputs_dict(__magic_name__					,							__magic_name__    )
							return config, inputs_dict






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  TFOPTModel(config=__magic_name__    )
							snake_case_						:    Optional[Any]				=  inputs_dict['''input_ids''']

							snake_case_						:    Optional[Any]				=  input_ids[:1, :]
							snake_case_						:    Tuple				=  inputs_dict['''attention_mask'''][:1, :]
							snake_case_						:    Optional[int]				=  1

							# first forward pass
							snake_case_						:    Optional[int]				=  model(__magic_name__					,							attention_mask=__magic_name__					,							use_cache=__magic_name__    )

							snake_case_			,							snake_case_						:    List[str]				=  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_						:    Any				=  ids_tensor((self.batch_size, 3)					,							config.vocab_size    )
							snake_case_						:    Any				=  tf.cast(ids_tensor((self.batch_size, 3)					,							2    )					,							tf.inta    )

							# append to next input_ids and
							snake_case_						:    Dict				=  tf.concat([input_ids, next_tokens]					,							axis=-1    )
							snake_case_						:    Optional[Any]				=  tf.concat([attention_mask, next_attn_mask]					,							axis=-1    )

							snake_case_						:    Dict				=  model(__magic_name__					,							attention_mask=__magic_name__    )[0]
							snake_case_						:    List[str]				=  model(__magic_name__					,							attention_mask=__magic_name__					,							past_key_values=__magic_name__    )[0]

							self.parent.assertEqual(next_tokens.shape[1]					,							output_from_past.shape[1]    )

							# select random slice
							snake_case_						:    Optional[int]				=  int(ids_tensor((1,)					,							output_from_past.shape[-1]    )    )
							snake_case_						:    List[str]				=  output_from_no_past[:, -3:, random_slice_idx]
							snake_case_						:    Dict				=  output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__magic_name__					,							__magic_name__					,							rtol=1e-3    )




@require_tf
class 					__lowerCAmelCase       (			_a,     _a,     unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
			lowerCamelCase_     :     List[str]       			=      (TFOPTForCausalLM,) if is_tf_available() else ()
			lowerCamelCase_     :     List[str]       			=      (
			    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
			)
			lowerCamelCase_     :     Dict       			=      False
			lowerCamelCase_     :     Optional[Any]       			=      False
			lowerCamelCase_     :     Dict       			=      False
			lowerCamelCase_     :     Union[str, Any]       			=      10






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  TFOPTModelTester(self    )
							snake_case_						:    Optional[int]				=  ConfigTester(self					,							config_class=__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_			,							snake_case_						:    Optional[int]				=  self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(__magic_name__					,							__magic_name__    ):
											if hasattr(__magic_name__					,							'''weight'''    ):
															return embedding_layer.weight
											else:
															# Here we build the word embeddings weights if not exists.
															# And then we retry to get the attribute once built.
															model.build()
															if hasattr(__magic_name__					,							'''weight'''    ):
																			return embedding_layer.weight
															else:
																			return None

							for model_class in self.all_model_classes:
											for size in [config.vocab_size - 10, config.vocab_size + 10]:
															# build the embeddings
															snake_case_						:    Union[str, Any]				=  model_class(config=__magic_name__    )
															snake_case_						:    Dict				=  _get_word_embedding_weight(__magic_name__					,							model.get_input_embeddings()    )
															snake_case_						:    Any				=  _get_word_embedding_weight(__magic_name__					,							model.get_output_embeddings()    )

															# reshape the embeddings
															model.resize_token_embeddings(__magic_name__    )
															snake_case_						:    List[str]				=  _get_word_embedding_weight(__magic_name__					,							model.get_input_embeddings()    )
															snake_case_						:    Any				=  _get_word_embedding_weight(__magic_name__					,							model.get_output_embeddings()    )

															# check that the resized embeddings size matches the desired size.
															snake_case_						:    Tuple				=  size if size is not None else config.vocab_size

															self.assertEqual(new_input_embeddings.shape[0]					,							__magic_name__    )

															# check that weights remain the same after resizing
															snake_case_						:    Optional[Any]				=  True
															for pa, pa in zip(old_input_embeddings.value()					,							new_input_embeddings.value()    ):
																			if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																							snake_case_						:    Optional[Any]				=  False
															self.assertTrue(__magic_name__    )

															if old_output_embeddings is not None and new_output_embeddings is not None:
																			self.assertEqual(new_output_embeddings.shape[0]					,							__magic_name__    )

																			snake_case_						:    str				=  True
																			for pa, pa in zip(old_output_embeddings.value()					,							new_output_embeddings.value()    ):
																							if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																											snake_case_						:    Optional[Any]				=  False
																			self.assertTrue(__magic_name__    )






def      lowerCamelCase_						(				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return tf.constant(_UpperCamelCase ,				dtype=tf.intaa )



@require_tf
class 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      99






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  tf.ones((4, 1)					,							dtype=tf.intaa    ) * 2
							snake_case_						:    Union[str, Any]				=  tf.concat([ids_tensor((4, 6)					,							self.vocab_size - 3    ) + 3, eos_column_vector]					,							axis=1    )
							snake_case_						:    Dict				=  input_ids.shape[0]
							snake_case_						:    Optional[Any]				=  OPTConfig(
							    vocab_size=self.vocab_size					,							hidden_size=24					,							num_hidden_layers=2					,							num_attention_heads=2					,							ffn_dim=32					,							max_position_embeddings=48					,							eos_token_id=2					,							pad_token_id=1					,							bos_token_id=0					,							)
							return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  TFOPTModel.from_pretrained('''facebook/opt-350m'''    )
							snake_case_						:    List[str]				=  _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    )
							snake_case_						:    List[Any]				=  tf.not_equal(__magic_name__					,							model.config.pad_token_id    )
							with tf.GradientTape():
											snake_case_						:    Union[str, Any]				=  model(input_ids=__magic_name__					,							attention_mask=__magic_name__    ).last_hidden_state
							snake_case_						:    Optional[Any]				=  (1, 11, 512)
							self.assertEqual(output.shape					,							__magic_name__    )
							snake_case_						:    List[Any]				=  tf.constant(
							    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]    )
							self.assertTrue(np.allclose(output[:, :3, :3]					,							__magic_name__					,							atol=4e-3    )    )

							snake_case_						:    Dict				=  tf.function(__magic_name__					,							jit_compile=__magic_name__    )
							snake_case_						:    Any				=  xla_generate(__magic_name__					,							__magic_name__    )[0]
							self.assertTrue(np.allclose(output[:, :3, :3]					,							__magic_name__					,							atol=4e-2    )    )




@require_tf
@slow
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							super().setUp()
							snake_case_						:    Any				=  '''facebook/opt-350m'''






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    int				=  TFOPTForCausalLM.from_pretrained(self.path_model    )
							snake_case_						:    str				=  GPTaTokenizer.from_pretrained(self.path_model    )

							snake_case_						:    int				=  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							snake_case_						:    List[Any]				=  tokenizer(__magic_name__					,							return_tensors='''tf'''					,							padding=__magic_name__					,							add_special_tokens=__magic_name__    )
							snake_case_						:    Dict				=  tf.math.reduce_mean(model(inputs.input_ids					,							attention_mask=inputs.attention_mask    )[0]					,							axis=-1    )
							snake_case_						:    Dict				=  tf.constant(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]    )
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-4    )    )

							snake_case_						:    Optional[int]				=  tf.function(__magic_name__					,							jit_compile=__magic_name__    )
							snake_case_						:    str				=  tf.math.reduce_mean(xla_generate(inputs.input_ids					,							attention_mask=inputs.attention_mask    )[0]					,							axis=-1    )
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-4    )    )




@require_tf
@slow
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			@property
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  '''facebook/opt-125m'''

							snake_case_						:    Tuple				=  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:    str				=  []
							snake_case_						:    Tuple				=  GPTaTokenizer.from_pretrained(__magic_name__    )
							snake_case_						:    List[Any]				=  TFOPTForCausalLM.from_pretrained(__magic_name__    )

							for prompt in self.prompts:
											snake_case_						:    Tuple				=  tokenizer(__magic_name__					,							return_tensors='''tf'''    ).input_ids

											snake_case_						:    str				=  model.generate(__magic_name__					,							max_length=10    )

											snake_case_						:    int				=  tokenizer.batch_decode(__magic_name__					,							skip_special_tokens=__magic_name__    )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    List[str]				=  '''facebook/opt-350m'''

							snake_case_						:    str				=  GPTaTokenizer.from_pretrained(__magic_name__    )
							snake_case_						:    str				=  TFOPTForCausalLM.from_pretrained(__magic_name__    )

							snake_case_						:    Union[str, Any]				=  '''left'''

							# use different length sentences to test batching
							snake_case_						:    List[str]				=  [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							snake_case_						:    int				=  tokenizer(__magic_name__					,							return_tensors='''tf'''					,							padding=__magic_name__    )
							snake_case_						:    List[Any]				=  inputs['''input_ids''']

							snake_case_						:    Tuple				=  model.generate(input_ids=__magic_name__					,							attention_mask=inputs['''attention_mask''']    )

							snake_case_						:    List[Any]				=  tokenizer(sentences[0]					,							return_tensors='''tf'''    ).input_ids
							snake_case_						:    int				=  model.generate(input_ids=__magic_name__    )

							snake_case_						:    Optional[int]				=  inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['''attention_mask'''][-1]					,							tf.intaa    )    )
							snake_case_						:    Any				=  tokenizer(sentences[1]					,							return_tensors='''tf'''    ).input_ids
							snake_case_						:    Dict				=  model.generate(input_ids=__magic_name__					,							max_length=model.config.max_length - num_paddings    )

							snake_case_						:    Union[str, Any]				=  tokenizer.batch_decode(__magic_name__					,							skip_special_tokens=__magic_name__    )
							snake_case_						:    Tuple				=  tokenizer.decode(output_non_padded[0]					,							skip_special_tokens=__magic_name__    )
							snake_case_						:    str				=  tokenizer.decode(output_padded[0]					,							skip_special_tokens=__magic_name__    )

							snake_case_						:    Tuple				=  [
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(__magic_name__					,							__magic_name__    )
							self.assertListEqual(__magic_name__					,							[non_padded_sentence, padded_sentence]    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  '''facebook/opt-350m'''

							snake_case_						:    Optional[Any]				=  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:    List[str]				=  []
							snake_case_						:    Union[str, Any]				=  GPTaTokenizer.from_pretrained(__magic_name__    )
							snake_case_						:    int				=  TFOPTForCausalLM.from_pretrained(__magic_name__    )

							for prompt in self.prompts:
											snake_case_						:    Dict				=  tokenizer(__magic_name__					,							return_tensors='''tf'''    ).input_ids

											snake_case_						:    Optional[int]				=  model.generate(__magic_name__					,							max_length=10    )

											snake_case_						:    Tuple				=  tokenizer.batch_decode(__magic_name__					,							skip_special_tokens=__magic_name__    )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__					,							__magic_name__    )







import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
