

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel







@require_tf
class   __lowercase      :
 _UpperCamelCase			  =  PegasusConfig
 _UpperCamelCase			  =  {}
 _UpperCamelCase			  =  """gelu"""
 def __init__(							self      ,							A_      ,							A_=13      ,							A_=7      ,							A_=True      ,							A_=False      ,							A_=99      ,							A_=32      ,							A_=2      ,							A_=4      ,							A_=37      ,							A_=0.1      ,							A_=0.1      ,							A_=40      ,							A_=2      ,							A_=1      ,							A_=0      ,							)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					parent
       __lowerCAmelCase					:						str					=					batch_size
       __lowerCAmelCase					:						Optional[Any]					=					seq_length
       __lowerCAmelCase					:						Optional[int]					=					is_training
       __lowerCAmelCase					:						Optional[int]					=					use_labels
       __lowerCAmelCase					:						Optional[int]					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					hidden_size
       __lowerCAmelCase					:						Dict					=					num_hidden_layers
       __lowerCAmelCase					:						int					=					num_attention_heads
       __lowerCAmelCase					:						int					=					intermediate_size

       __lowerCAmelCase					:						Optional[Any]					=					hidden_dropout_prob
       __lowerCAmelCase					:						Union[str, Any]					=					attention_probs_dropout_prob
       __lowerCAmelCase					:						List[Any]					=					max_position_embeddings
       __lowerCAmelCase					:						List[str]					=					eos_token_id
       __lowerCAmelCase					:						Optional[int]					=					pad_token_id
       __lowerCAmelCase					:						Optional[Any]					=					bos_token_id
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						List[str]					=					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size					)

       __lowerCAmelCase					:						Any					=					self.config_cls(
           vocab_size=self.vocab_size      ,							d_model=self.hidden_size      ,							encoder_layers=self.num_hidden_layers      ,							decoder_layers=self.num_hidden_layers      ,							encoder_attention_heads=self.num_attention_heads      ,							decoder_attention_heads=self.num_attention_heads      ,							encoder_ffn_dim=self.intermediate_size      ,							decoder_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_ids=[2]      ,							bos_token_id=self.bos_token_id      ,							pad_token_id=self.pad_token_id      ,							decoder_start_token_id=self.pad_token_id      ,							**self.config_updates      ,							)
       __lowerCAmelCase					:						List[Any]					=					prepare_pegasus_inputs_dict(A_      ,							A_      ,							A_					)
       return config, inputs_dict





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					TFPegasusModel(config=A_					).get_decoder()
       __lowerCAmelCase					:						Optional[Any]					=					inputs_dict['''input_ids''']

       __lowerCAmelCase					:						Optional[Any]					=					input_ids[:1, :]
       __lowerCAmelCase					:						Union[str, Any]					=					inputs_dict['''attention_mask'''][:1, :]
       __lowerCAmelCase					:						Optional[int]					=					inputs_dict['''head_mask''']
       __lowerCAmelCase					:						Optional[Any]					=					1

       # first forward pass
       __lowerCAmelCase					:						Union[str, Any]					=					model(A_      ,							attention_mask=A_      ,							head_mask=A_      ,							use_cache=A_					)

       __lowerCAmelCase,							__lowerCAmelCase					:						Optional[int]					=					outputs.to_tuple()

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

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

       __lowerCAmelCase					:						Any					=					model(A_      ,							attention_mask=A_					)[0]
       __lowerCAmelCase					:						Union[str, Any]					=					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					:						Dict					=					int(ids_tensor((1,)      ,							output_from_past.shape[-1]					)					)
       __lowerCAmelCase					:						str					=					output_from_no_past[:, -3:, random_slice_idx]
       __lowerCAmelCase					:						Union[str, Any]					=					output_from_past[:, :, random_slice_idx]

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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=None   ,	):
      if attention_mask is None:
            __lowerCAmelCase					:						int					=					tf.cast(tf.math.not_equal(lowercase__   ,	config.pad_token_id						)   ,	tf.inta						)
      if decoder_attention_mask is None:
            __lowerCAmelCase					:						Any					=					tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape   ,	dtype=tf.inta						),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,	config.pad_token_id						)   ,	tf.inta						),
                ]   ,	axis=-1   ,	)
      if head_mask is None:
            __lowerCAmelCase					:						Any					=					tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            __lowerCAmelCase					:						Optional[Any]					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            __lowerCAmelCase					:						List[Any]					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }







@require_tf
class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 _UpperCamelCase			  =  (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 _UpperCamelCase			  =  (
     {
         """conversational""": TFPegasusForConditionalGeneration,
         """feature-extraction""": TFPegasusModel,
         """summarization""": TFPegasusForConditionalGeneration,
         """text2text-generation""": TFPegasusForConditionalGeneration,
         """translation""": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  False
 _UpperCamelCase			  =  False
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					TFPegasusModelTester(self					)
       __lowerCAmelCase					:						str					=					ConfigTester(self      ,							config_class=A_					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       self.config_tester.run_common_tests()





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



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_					)







@require_sentencepiece
@require_tokenizers
@require_tf
class   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  [
     """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
     """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
 ]
 _UpperCamelCase			  =  [
     """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
     """ reduce the risk of wildfires.""",
     """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
 ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
 _UpperCamelCase			  =  """google/pegasus-xsum"""
 @cached_property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       return AutoTokenizer.from_pretrained(self.model_name					)
 @cached_property
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       return model
 def 	UpperCamelCase__  (							self      ,							**A_					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.translate_src_text(**A_					)
       assert self.expected_text == generated_words
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.tokenizer(self.src_text      ,							**A_      ,							padding=A_      ,							return_tensors='''tf'''					)
       __lowerCAmelCase					:						Dict					=					self.model.generate(
           model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							use_cache=A_      ,							)
       __lowerCAmelCase					:						Dict					=					self.tokenizer.batch_decode(generated_ids.numpy()      ,							skip_special_tokens=A_					)
       return generated_words





 @slow
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       self._assert_generated_batch_equal_expected()





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


