

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel


@require_tf
class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			__A    	=	BlenderbotSmallConfig
			__A    	=	{}
			__A    	=	"""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"""


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

				snake_case_   		=			hidden_dropout_prob
				snake_case_   		=			attention_probs_dropout_prob
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			eos_token_id
				snake_case_   		=			pad_token_id
				snake_case_   		=			bos_token_id


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

				snake_case_   		=			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			,					)
				snake_case_   		=			prepare_blenderbot_small_inputs_dict(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				return config, inputs_dict







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			TFBlenderbotSmallModel(config=__UpperCamelCase				).get_decoder()
				snake_case_   		=			inputs_dict['input_ids']

				snake_case_   		=			input_ids[:1, :]
				snake_case_   		=			inputs_dict['attention_mask'][:1, :]
				snake_case_   		=			inputs_dict['head_mask']
				snake_case_   		=			1

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

				snake_case_		,							snake_case_   		=			outputs.to_tuple()

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

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

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

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

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

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







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__=None      ,			lowercase__=None      ,			lowercase__=None      ,			lowercase__=None      ,			lowercase__=None      ,			):





	'''simple docstring'''
	if attention_mask is None:
		snake_case_   		=			tf.cast(tf.math.not_equal(lowercase__      ,			config.pad_token_id						)      ,			tf.inta						)
	if decoder_attention_mask is None:
		snake_case_   		=			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:
		snake_case_   		=			tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
	if decoder_head_mask is None:
		snake_case_   		=			tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
	if cross_attn_head_mask is None:
		snake_case_   		=			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 SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			__A    	=	(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			__A    	=	(
			    {
			        """conversational""": TFBlenderbotSmallForConditionalGeneration,
			        """feature-extraction""": TFBlenderbotSmallModel,
			        """summarization""": TFBlenderbotSmallForConditionalGeneration,
			        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
			        """translation""": TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__A    	=	True
			__A    	=	False
			__A    	=	False


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			TFBlenderbotSmallModelTester(self				)
				snake_case_   		=			ConfigTester(self			,					config_class=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.config_tester.run_common_tests()







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







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






			"""simple docstring"""




			__A    	=	[
			    """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
			    """  i'm going to throw up.\nand why is that?"""
			]
			__A    	=	"""facebook/blenderbot_small-90M"""


			@cached_property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)


			@cached_property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.tokenizer(self.src_text			,					return_tensors='tf'				)
				snake_case_   		=			self.model.generate(
				    model_inputs.input_ids			,					attention_mask=model_inputs.attention_mask			,					num_beams=2			,					use_cache=__UpperCamelCase			,					)
				snake_case_   		=			self.tokenizer.batch_decode(generated_ids.numpy()			,					skip_special_tokens=__UpperCamelCase				)[0]
				assert generated_words in (
				    "i don't know. i just feel like i'm going to throw up. it's not fun.",
				    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
				    "i'm not sure. i just feel like i've been in a bad situation.",
				)



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
