
"""simple docstring"""

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 __lowerCAmelCase	:
					lowercase		     =		BlenderbotConfig
					lowercase		     =		{}
					lowercase		     =		"gelu"





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

						'''simple docstring'''




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

						__UpperCamelCase       						= hidden_dropout_prob
						__UpperCamelCase       						= attention_probs_dropout_prob
						__UpperCamelCase       						= max_position_embeddings
						__UpperCamelCase       						= eos_token_id
						__UpperCamelCase       						= pad_token_id
						__UpperCamelCase       						= bos_token_id





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




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

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

						__UpperCamelCase       						= 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			,  )
						__UpperCamelCase       						= prepare_blenderbot_inputs_dict(_lowercase			,  _lowercase			,  _lowercase   )
						return config, inputs_dict







					def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

						'''simple docstring'''




						__UpperCamelCase       						= TFBlenderbotModel(config=_lowercase   ).get_decoder()
						__UpperCamelCase       						= inputs_dict["""input_ids"""]

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

						# first forward pass
						__UpperCamelCase       						= model(_lowercase			,  attention_mask=_lowercase			,  head_mask=_lowercase			,  use_cache=_lowercase   )

						__UpperCamelCase       						= outputs.to_tuple()

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

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

						__UpperCamelCase       						= model(_lowercase			,  attention_mask=_lowercase   )[0]
						__UpperCamelCase       						= model(_lowercase			,  attention_mask=_lowercase			,  past_key_values=_lowercase   )[0]

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

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

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






def 							A		(       snake_case   :List[Any]       ,      snake_case   :Optional[int]       ,      snake_case   :List[Any]       ,      snake_case   :Optional[int]=None       ,      snake_case   :Union[str, Any]=None       ,      snake_case   :List[Any]=None       ,      snake_case   :Tuple=None       ,      snake_case   :Union[str, Any]=None       ,      )   ->			Union[str, Any]:






	if attention_mask is None:
		__UpperCamelCase       						= tf.cast(tf.math.not_equal(__UpperCamelCase       ,      config.pad_token_id	)       ,      tf.inta	)
	if decoder_attention_mask is None:
		__UpperCamelCase       						= 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:
		__UpperCamelCase       						= tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
	if decoder_head_mask is None:
		__UpperCamelCase       						= tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
	if cross_attn_head_mask is None:
		__UpperCamelCase       						= 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 __lowerCAmelCase	(   SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						unittest.TestCase    ):
					lowercase		     =		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					lowercase		     =		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					lowercase		     =		(
					    {
					        "conversational": TFBlenderbotForConditionalGeneration,
					        "feature-extraction": TFBlenderbotModel,
					        "summarization": TFBlenderbotForConditionalGeneration,
					        "text2text-generation": TFBlenderbotForConditionalGeneration,
					        "translation": TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowercase		     =		True
					lowercase		     =		False
					lowercase		     =		False





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= TFBlenderbotModelTester(self   )
						__UpperCamelCase       						= ConfigTester(self			,  config_class=_lowercase   )





					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						self.config_tester.run_common_tests()







					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




						__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*_lowercase   )


@require_tokenizers
@require_tf
class __lowerCAmelCase	(   unittest.TestCase    ):
					lowercase		     =		["My friends are cool but they eat too many carbs."]
					lowercase		     =		"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'''




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







					@slow
					def 		UpperCAmelCase      (			self   ):

						'''simple docstring'''




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





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       Dict									=				{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[Any]									=				[
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      UpperCamelCase			:       Any									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



