



from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel



@require_tf
class 	_lowercase							:





	"""simple docstring"""
	__A							=							BlenderbotConfig
	__A							=							{}
	__A							=							"gelu"




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=13		,       lowerCamelCase_=7		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=99		,       lowerCamelCase_=32		,       lowerCamelCase_=2		,       lowerCamelCase_=4		,       lowerCamelCase_=37		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=20		,       lowerCamelCase_=2		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   seq_length
						a					     =   is_training
						a					     =   use_labels
						a					     =   vocab_size
						a					     =   hidden_size
						a					     =   num_hidden_layers
						a					     =   num_attention_heads
						a					     =   intermediate_size

						a					     =   hidden_dropout_prob
						a					     =   attention_probs_dropout_prob
						a					     =   max_position_embeddings
						a					     =   eos_token_id
						a					     =   pad_token_id
						a					     =   bos_token_id




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size		)

						a					     =   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		,       )
						a					     =   prepare_blenderbot_inputs_dict(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						return config, inputs_dict




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   TFBlenderbotModel(config=lowerCamelCase_		).get_decoder()
						a					     =   inputs_dict["input_ids"]

						a					     =   input_ids[:1, :]
						a					     =   inputs_dict["attention_mask"][:1, :]
						a					     =   inputs_dict["head_mask"]
						a					     =   1

						# first forward pass
						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       head_mask=lowerCamelCase_		,       use_cache=lowerCamelCase_		)

						a     ,				a					     =   outputs.to_tuple()

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

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

						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		)[0]
						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       past_key_values=lowerCamelCase_		)[0]

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

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

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






def     a(      A       :    Optional[Any] ,     A       :    int ,     A       :    Optional[Any] ,     A       :    Tuple=None ,     A       :    str=None ,     A       :    int=None ,     A       :    Dict=None ,     A       :    Union[str, Any]=None ,     )		->       Optional[int]:
					"""simple docstring"""





					if attention_mask is None:
										a					     =   tf.cast(tf.math.not_equal(A ,     config.pad_token_id       ) ,     tf.inta       )
					if decoder_attention_mask is None:
										a					     =   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:
										a					     =   tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
					if decoder_head_mask is None:
										a					     =   tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
					if cross_attn_head_mask is None:
										a					     =   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							(   lowerCAmelCase,      lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
	__A							=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
	__A							=							(
	    {
	        "conversational": TFBlenderbotForConditionalGeneration,
	        "feature-extraction": TFBlenderbotModel,
	        "summarization": TFBlenderbotForConditionalGeneration,
	        "text2text-generation": TFBlenderbotForConditionalGeneration,
	        "translation": TFBlenderbotForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__A							=							True
	__A							=							False
	__A							=							False




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFBlenderbotModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_		)




@require_tokenizers
@require_tf
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""
	__A							=							["My friends are cool but they eat too many carbs."]
	__A							=							"facebook/blenderbot-400M-distill"




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return BlenderbotTokenizer.from_pretrained(self.model_name		)




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
						return model




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.tokenizer(self.src_text		,       return_tensors="tf"		)
						a					     =   self.model.generate(
						    model_inputs.input_ids		,       )
						a					     =   self.tokenizer.batch_decode(generated_ids.numpy()		,       skip_special_tokens=lowerCamelCase_		)[0]
						assert (
						    generated_words
						    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
						)





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


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"trocr"
	__A							=							["past_key_values"]
	__A							=							{
	    "num_attention_heads": "decoder_attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "decoder_layers",
	}




	def __init__(self		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   d_model
						a					     =   decoder_layers
						a					     =   decoder_attention_heads
						a					     =   decoder_ffn_dim
						a					     =   activation_function
						a					     =   max_position_embeddings
						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   init_std
						a					     =   decoder_layerdrop
						a					     =   use_cache
						a					     =   scale_embedding
						a					     =   use_learned_position_embeddings
						a					     =   layernorm_embedding

						super().__init__(
						    pad_token_id=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
