'''simple docstring'''

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel




@require_tf
class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			__magic_name__ :				str   							=       MarianConfig
			__magic_name__ :				Optional[int]   							=       {}
			__magic_name__ :				Dict   							=       """gelu"""



			def __init__(							self				,			snake_case_				,			snake_case_=13				,			snake_case_=7				,			snake_case_=True				,			snake_case_=False				,			snake_case_=99				,			snake_case_=32				,			snake_case_=2				,			snake_case_=4				,			snake_case_=37				,			snake_case_=0.1				,			snake_case_=0.1				,			snake_case_=20				,			snake_case_=2				,			snake_case_=1				,			snake_case_=0				,			):
									snake_case :       Any				=    parent
									snake_case :       Optional[int]				=    batch_size
									snake_case :       List[Any]				=    seq_length
									snake_case :       Tuple				=    is_training
									snake_case :       str				=    use_labels
									snake_case :       Tuple				=    vocab_size
									snake_case :       List[str]				=    hidden_size
									snake_case :       Optional[Any]				=    num_hidden_layers
									snake_case :       Dict				=    num_attention_heads
									snake_case :       Optional[int]				=    intermediate_size

									snake_case :       Optional[int]				=    hidden_dropout_prob
									snake_case :       Optional[Any]				=    attention_probs_dropout_prob
									snake_case :       List[str]				=    max_position_embeddings
									snake_case :       Optional[int]				=    eos_token_id
									snake_case :       str				=    pad_token_id
									snake_case :       Union[str, Any]				=    bos_token_id



			def   lowerCamelCase (							self					):
									snake_case :       str				=    ids_tensor([self.batch_size, self.seq_length - 1]				,			self.vocab_size					)
									snake_case :       Tuple				=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)				,			1					)
									snake_case :       Tuple				=    tf.concat([input_ids, eos_tensor]				,			axis=1					)

									snake_case :       Optional[int]				=    ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size					)

									snake_case :       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				,			)
									snake_case :       Union[str, Any]				=    prepare_marian_inputs_dict(snake_case_				,			snake_case_				,			snake_case_					)
									return config, inputs_dict






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    TFMarianModel(config=snake_case_					).get_decoder()
									snake_case :       List[str]				=    inputs_dict["input_ids"]

									snake_case :       Optional[Any]				=    input_ids[:1, :]
									snake_case :       List[Any]				=    inputs_dict["attention_mask"][:1, :]
									snake_case :       Optional[int]				=    inputs_dict["head_mask"]
									snake_case :       Union[str, Any]				=    1

									# first forward pass
									snake_case :       Any				=    model(snake_case_				,			attention_mask=snake_case_				,			head_mask=snake_case_				,			use_cache=snake_case_					)

									snake_case				,					snake_case :       int				=    outputs.to_tuple()

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

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

									snake_case :       List[str]				=    model(snake_case_				,			attention_mask=snake_case_					)[0]
									snake_case :       Optional[int]				=    model(snake_case_				,			attention_mask=snake_case_				,			past_key_values=snake_case_					)[0]

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

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

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







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			str,      _lowerCAmelCase    :			Optional[Any]=None,      _lowerCAmelCase    :			int=None,      _lowerCAmelCase    :			Tuple=None,      _lowerCAmelCase    :			Optional[Any]=None,      _lowerCAmelCase    :			List[str]=None,      )    ->		Tuple:



						"""simple docstring"""

						if attention_mask is None:
												snake_case :       Optional[int]				=    tf.cast(tf.math.not_equal(_lowerCAmelCase,      config.pad_token_id),      tf.inta)
						if decoder_attention_mask is None:
												snake_case :       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:
												snake_case :       Tuple				=    tf.ones((config.encoder_layers, config.encoder_attention_heads))
						if decoder_head_mask is None:
												snake_case :       str				=    tf.ones((config.decoder_layers, config.decoder_attention_heads))
						if cross_attn_head_mask is None:
												snake_case :       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 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase							,				_UpperCAmelCase							,				unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
			__magic_name__ :				Tuple   							=       (TFMarianMTModel,) if is_tf_available() else ()
			__magic_name__ :				Dict   							=       (
			    {
			        """conversational""": TFMarianMTModel,
			        """feature-extraction""": TFMarianModel,
			        """summarization""": TFMarianMTModel,
			        """text2text-generation""": TFMarianMTModel,
			        """translation""": TFMarianMTModel,
			    }
			    if is_tf_available()
			    else {}
			)
			__magic_name__ :				List[Any]   							=       True
			__magic_name__ :				List[Any]   							=       False
			__magic_name__ :				Optional[Any]   							=       False



			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    TFMarianModelTester(self					)
									snake_case :       str				=    ConfigTester(self				,			config_class=snake_case_					)



			def   lowerCamelCase (							self					):
									self.config_tester.run_common_tests()






			def   lowerCamelCase (							self					):
									snake_case :       Any				=    self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*snake_case_					)







@require_tf
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				int   							=       1_000  # show more chars for failing integration tests



			@classmethod
			def   lowerCamelCase (							cls					):
									snake_case :       Optional[Any]				=    F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls



			@cached_property
			def   lowerCamelCase (							self					):
									return AutoTokenizer.from_pretrained(self.model_name					)



			@property
			def   lowerCamelCase (							self					):
									return self.tokenizer.eos_token_id



			@cached_property
			def   lowerCamelCase (							self					):
									warnings.simplefilter("error"					)
									snake_case :       TFMarianMTModel				=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
									assert isinstance(snake_case_				,			snake_case_					)
									snake_case :       Tuple				=    model.config
									self.assertListEqual(c.bad_words_ids				,			[[c.pad_token_id]]					)
									self.assertEqual(c.max_length				,			512					)
									self.assertEqual(c.decoder_start_token_id				,			c.pad_token_id					)
									return model



			def   lowerCamelCase (							self				,			**snake_case_					):
									snake_case :       Optional[int]				=    self.translate_src_text(**snake_case_					)
									self.assertListEqual(self.expected_text				,			snake_case_					)






			def   lowerCamelCase (							self				,			**snake_case_					):
									snake_case :       List[Any]				=    self.tokenizer(self.src_text				,			**snake_case_				,			padding=snake_case_				,			return_tensors="tf"					)
									snake_case :       List[str]				=    self.model.generate(
									    model_inputs.input_ids				,			attention_mask=model_inputs.attention_mask				,			num_beams=2				,			max_length=128					)
									snake_case :       Optional[Any]				=    self.tokenizer.batch_decode(generated_ids.numpy()				,			skip_special_tokens=snake_case_					)
									return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       """mt"""
			__magic_name__ :				str   							=       """en"""
			__magic_name__ :				List[Any]   							=       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
			__magic_name__ :				Union[str, Any]   							=       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]



			@unittest.skip("Skipping until #12647 is resolved."					)
			@slow
			def   lowerCamelCase (							self					):
									self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       """en"""
			__magic_name__ :				Optional[int]   							=       """zh"""
			__magic_name__ :				List[str]   							=       ["""My name is Wolfgang and I live in Berlin"""]
			__magic_name__ :				Any   							=       ["""我叫沃尔夫冈 我住在柏林"""]



			@unittest.skip("Skipping until #12647 is resolved."					)
			@slow
			def   lowerCamelCase (							self					):
									self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       """en"""
			__magic_name__ :				Any   							=       """ROMANCE"""
			__magic_name__ :				Dict   							=       [
			    """>>fr<< Don't spend so much time watching TV.""",
			    """>>pt<< Your message has been sent.""",
			    """>>es<< He's two years older than me.""",
			]
			__magic_name__ :				Tuple   							=       [
			    """Ne passez pas autant de temps à regarder la télé.""",
			    """A sua mensagem foi enviada.""",
			    """Es dos años más viejo que yo.""",
			]



			@unittest.skip("Skipping until #12647 is resolved."					)
			@slow
			def   lowerCamelCase (							self					):
									self._assert_generated_batch_equal_expected()






			@unittest.skip("Skipping until #12647 is resolved."					)
			@slow
			def   lowerCamelCase (							self					):
									snake_case :       str				=    TranslationPipeline(self.model				,			self.tokenizer				,			framework="tf"					)
									snake_case :       Optional[Any]				=    pipeline(self.src_text					)
									self.assertEqual(self.expected_text				,			[x["translation_text"] for x in output]					)






'''simple docstring'''

# 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_torch_available,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextV2ForImageClassification",
						    "ConvNextV2Model",
						    "ConvNextV2PreTrainedModel",
						    "ConvNextV2Backbone",
						]


if TYPE_CHECKING:
						from .configuration_convnextva import (
						    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConvNextVaConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnextva import (
												    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextVaBackbone,
												    ConvNextVaForImageClassification,
												    ConvNextVaModel,
												    ConvNextVaPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure)





