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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None		)						->    Any:



      '''simple docstring'''

      if attention_mask is None:
            _lowerCamelCase			    =				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  UpperCamelCase				:

     '''simple docstring'''

     A_										=  OPTConfig
     A_										=  {}
     A_										=  '''gelu'''



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






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				seq_length
           _lowerCamelCase			    =				is_training
           _lowerCamelCase			    =				use_labels
           _lowerCamelCase			    =				vocab_size
           _lowerCamelCase			    =				hidden_size
           _lowerCamelCase			    =				num_hidden_layers
           _lowerCamelCase			    =				num_attention_heads
           _lowerCamelCase			    =				intermediate_size
           _lowerCamelCase			    =				hidden_act
           _lowerCamelCase			    =				hidden_dropout_prob
           _lowerCamelCase			    =				attention_probs_dropout_prob
           _lowerCamelCase			    =				max_position_embeddings
           _lowerCamelCase			    =				eos_token_id
           _lowerCamelCase			    =				pad_token_id
           _lowerCamelCase			    =				bos_token_id
           _lowerCamelCase			    =				embed_dim
           _lowerCamelCase			    =				word_embed_proj_dim
           _lowerCamelCase			    =				False



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size						)
           _lowerCamelCase			    =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)				,				1						)
           _lowerCamelCase			    =				tf.concat([input_ids, eos_tensor]				,				axis=1						)

           _lowerCamelCase			    =				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=__a				,				**self.config_updates				,				)
           _lowerCamelCase			    =				prepare_opt_inputs_dict(__a				,				__a						)
           return config, inputs_dict





     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				TFOPTModel(config=__a						)
           _lowerCamelCase			    =				inputs_dict["""input_ids"""]

           _lowerCamelCase			    =				input_ids[:1, :]
           _lowerCamelCase			    =				inputs_dict["""attention_mask"""][:1, :]
           _lowerCamelCase			    =				1

           # first forward pass
           _lowerCamelCase			    =				model(__a				,				attention_mask=__a				,				use_cache=__a						)

           _lowerCamelCase			    =				outputs.to_tuple()

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

           # append to next input_ids and
           _lowerCamelCase			    =				tf.concat([input_ids, next_tokens]				,				axis=-1						)
           _lowerCamelCase			    =				tf.concat([attention_mask, next_attn_mask]				,				axis=-1						)

           _lowerCamelCase			    =				model(__a				,				attention_mask=__a						)[0]
           _lowerCamelCase			    =				model(__a				,				attention_mask=__a				,				past_key_values=__a						)[0]

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

           # select random slice
           _lowerCamelCase			    =				int(ids_tensor((1,)				,				output_from_past.shape[-1]						)						)
           _lowerCamelCase			    =				output_from_no_past[:, -3:, random_slice_idx]
           _lowerCamelCase			    =				output_from_past[:, :, random_slice_idx]

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





@require_tf
class  UpperCamelCase				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     A_										=  (TFOPTForCausalLM,) if is_tf_available() else ()
     A_										=  (
         {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
     )
     A_										=  False
     A_										=  False
     A_										=  False
     A_										=  10



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				TFOPTModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=__a						)



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






           """simple docstring"""
           self.config_tester.run_common_tests()



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__a						)





     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(A_				,				A_						):
                 if hasattr(__a				,				'''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(__a				,				'''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
                       _lowerCamelCase			    =				model_class(config=__a						)
                       _lowerCamelCase			    =				_get_word_embedding_weight(__a				,				model.get_input_embeddings()						)
                       _lowerCamelCase			    =				_get_word_embedding_weight(__a				,				model.get_output_embeddings()						)

                       # reshape the embeddings
                       model.resize_token_embeddings(__a						)
                       _lowerCamelCase			    =				_get_word_embedding_weight(__a				,				model.get_input_embeddings()						)
                       _lowerCamelCase			    =				_get_word_embedding_weight(__a				,				model.get_output_embeddings()						)

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

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

                       # check that weights remain the same after resizing
                       _lowerCamelCase			    =				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:
                                   _lowerCamelCase			    =				False
                       self.assertTrue(__a						)

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

                             _lowerCamelCase			    =				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:
                                         _lowerCamelCase			    =				False
                             self.assertTrue(__a						)





def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

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



@require_tf
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     A_										=  99



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






           """simple docstring"""
           _lowerCamelCase			    =				tf.ones((4, 1)				,				dtype=tf.intaa						) * 2
           _lowerCamelCase			    =				tf.concat([ids_tensor((4, 6)				,				self.vocab_size - 3						) + 3, eos_column_vector]				,				axis=1						)
           _lowerCamelCase			    =				input_ids.shape[0]
           _lowerCamelCase			    =				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  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				TFOPTModel.from_pretrained('''facebook/opt-350m'''						)
           _lowerCamelCase			    =				_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
           _lowerCamelCase			    =				tf.not_equal(__a				,				model.config.pad_token_id						)
           with tf.GradientTape():
                 _lowerCamelCase			    =				model(input_ids=__a				,				attention_mask=__a						).last_hidden_state
           _lowerCamelCase			    =				(1, 11, 5_12)
           self.assertEqual(output.shape				,				__a						)
           _lowerCamelCase			    =				tf.constant(
               [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]						)
           self.assertTrue(np.allclose(output[:, :3, :3]				,				__a				,				atol=4E-3						)						)

           _lowerCamelCase			    =				tf.function(__a				,				jit_compile=__a						)
           _lowerCamelCase			    =				xla_generate(__a				,				__a						)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]				,				__a				,				atol=4E-2						)						)





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

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           super().setUp()
           _lowerCamelCase			    =				"""facebook/opt-350m"""





     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				TFOPTForCausalLM.from_pretrained(self.path_model						)
           _lowerCamelCase			    =				GPTaTokenizer.from_pretrained(self.path_model						)

           _lowerCamelCase			    =				[
               """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
           _lowerCamelCase			    =				tokenizer(__a				,				return_tensors='''tf'''				,				padding=__a				,				add_special_tokens=__a						)
           _lowerCamelCase			    =				tf.math.reduce_mean(model(inputs.input_ids				,				attention_mask=inputs.attention_mask						)[0]				,				axis=-1						)
           _lowerCamelCase			    =				tf.constant(
               [
                   [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                   [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                   [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                   [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
               ]						)
           self.assertTrue(np.allclose(__a				,				__a				,				atol=1E-4						)						)

           _lowerCamelCase			    =				tf.function(__a				,				jit_compile=__a						)
           _lowerCamelCase			    =				tf.math.reduce_mean(xla_generate(inputs.input_ids				,				attention_mask=inputs.attention_mask						)[0]				,				axis=-1						)
           self.assertTrue(np.allclose(__a				,				__a				,				atol=1E-4						)						)





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

     '''simple docstring'''

     @property
     def 		UpperCamelCase_						(					self						) ->				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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				"""facebook/opt-125m"""

           _lowerCamelCase			    =				[
               """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""",
           ]

           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				GPTaTokenizer.from_pretrained(__a						)
           _lowerCamelCase			    =				TFOPTForCausalLM.from_pretrained(__a						)

           for prompt in self.prompts:
                 _lowerCamelCase			    =				tokenizer(__a				,				return_tensors='''tf'''						).input_ids

                 _lowerCamelCase			    =				model.generate(__a				,				max_length=10						)

                 _lowerCamelCase			    =				tokenizer.batch_decode(__a				,				skip_special_tokens=__a						)
                 predicted_outputs += generated_string

           self.assertListEqual(__a				,				__a						)



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






           """simple docstring"""
           _lowerCamelCase			    =				"""facebook/opt-350m"""

           _lowerCamelCase			    =				GPTaTokenizer.from_pretrained(__a						)
           _lowerCamelCase			    =				TFOPTForCausalLM.from_pretrained(__a						)

           _lowerCamelCase			    =				"""left"""

           # use different length sentences to test batching
           _lowerCamelCase			    =				[
               """Hello, my dog is a little""",
               """Today, I""",
           ]

           _lowerCamelCase			    =				tokenizer(__a				,				return_tensors='''tf'''				,				padding=__a						)
           _lowerCamelCase			    =				inputs["""input_ids"""]

           _lowerCamelCase			    =				model.generate(input_ids=__a				,				attention_mask=inputs['''attention_mask''']						)

           _lowerCamelCase			    =				tokenizer(sentences[0]				,				return_tensors='''tf'''						).input_ids
           _lowerCamelCase			    =				model.generate(input_ids=__a						)

           _lowerCamelCase			    =				inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs['''attention_mask'''][-1]				,				tf.intaa						)						)
           _lowerCamelCase			    =				tokenizer(sentences[1]				,				return_tensors='''tf'''						).input_ids
           _lowerCamelCase			    =				model.generate(input_ids=__a				,				max_length=model.config.max_length - num_paddings						)

           _lowerCamelCase			    =				tokenizer.batch_decode(__a				,				skip_special_tokens=__a						)
           _lowerCamelCase			    =				tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=__a						)
           _lowerCamelCase			    =				tokenizer.decode(output_padded[0]				,				skip_special_tokens=__a						)

           _lowerCamelCase			    =				[
               """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(__a				,				__a						)
           self.assertListEqual(__a				,				[non_padded_sentence, padded_sentence]						)





     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				"""facebook/opt-350m"""

           _lowerCamelCase			    =				[
               """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""",
           ]

           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				GPTaTokenizer.from_pretrained(__a						)
           _lowerCamelCase			    =				TFOPTForCausalLM.from_pretrained(__a						)

           for prompt in self.prompts:
                 _lowerCamelCase			    =				tokenizer(__a				,				return_tensors='''tf'''						).input_ids

                 _lowerCamelCase			    =				model.generate(__a				,				max_length=10						)

                 _lowerCamelCase			    =				tokenizer.batch_decode(__a				,				skip_special_tokens=__a						)
                 predicted_outputs += generated_string

           self.assertListEqual(__a				,				__a						)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)