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 	_a  :



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					MarianConfig
	__SCREAMING_SNAKE_CASE              =					{}
	__SCREAMING_SNAKE_CASE              =					'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       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =seq_length
		_lowercase   =is_training
		_lowercase   =use_labels
		_lowercase   =vocab_size
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =intermediate_size

		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =max_position_embeddings
		_lowercase   =eos_token_id
		_lowercase   =pad_token_id
		_lowercase   =bos_token_id


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

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

		_lowercase   =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       ,  )
		_lowercase   =prepare_marian_inputs_dict(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		return config, inputs_dict


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =TFMarianModel(config=lowerCAmelCase_       ).get_decoder()
		_lowercase   =inputs_dict["input_ids"]

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

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

		_lowercase   ,  _lowercase   =outputs.to_tuple()

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

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

		_lowercase   =model(lowerCAmelCase_       ,  attention_mask=lowerCAmelCase_       )[0]
		_lowercase   =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
		_lowercase   =int(ids_tensor((1,)       ,  output_from_past.shape[-1]       )       )
		_lowercase   =output_from_no_past[:, -3:, random_slice_idx]
		_lowercase   =output_from_past[:, :, random_slice_idx]

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

def 	__lowerCamelCase							(				__a    : Dict      ,							__a    : Dict      ,							__a    : Tuple      ,							__a    : Any=None      ,							__a    : Any=None      ,							__a    : Dict=None      ,							__a    : int=None      ,							__a    : Dict=None      ,							)      ->       List[Any]:
	if attention_mask is None:
		_lowercase   =tf.cast(tf.math.not_equal(__a      ,							config.pad_token_id		)      ,							tf.inta		)
	if decoder_attention_mask is None:
		_lowercase   =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:
		_lowercase   =tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
	if decoder_head_mask is None:
		_lowercase   =tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
	if cross_attn_head_mask is None:
		_lowercase   =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 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE              =					(TFMarianMTModel,) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE              =					(
	    {
	        'conversational': TFMarianMTModel,
	        'feature-extraction': TFMarianModel,
	        'summarization': TFMarianMTModel,
	        'text2text-generation': TFMarianMTModel,
	        'translation': TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE              =					True
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =TFMarianModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       )


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_       )

@require_tf
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					10_00  # show more chars for failing integration tests


	@classmethod
	def __lowerCAmelCase      (   cls       ):
		_lowercase   =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"       )
		_lowercase   =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
		assert isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =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       ,  **lowerCAmelCase_       ):
		_lowercase   =self.translate_src_text(**lowerCAmelCase_       )
		self.assertListEqual(self.expected_text       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.tokenizer(self.src_text       ,  **lowerCAmelCase_       ,  padding=lowerCAmelCase_       ,  return_tensors="tf"       )
		_lowercase   =self.model.generate(
		    model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2       ,  max_length=128       )
		_lowercase   =self.tokenizer.batch_decode(generated_ids.numpy()       ,  skip_special_tokens=lowerCAmelCase_       )
		return generated_words

@require_sentencepiece
@require_tokenizers
@require_tf
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'mt'
	__SCREAMING_SNAKE_CASE              =					'en'
	__SCREAMING_SNAKE_CASE              =					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
	__SCREAMING_SNAKE_CASE              =					['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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'en'
	__SCREAMING_SNAKE_CASE              =					'zh'
	__SCREAMING_SNAKE_CASE              =					['My name is Wolfgang and I live in Berlin']
	__SCREAMING_SNAKE_CASE              =					['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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'en'
	__SCREAMING_SNAKE_CASE              =					'ROMANCE'
	__SCREAMING_SNAKE_CASE              =					[
	    '>>fr<< Don\'t spend so much time watching TV.',
	    '>>pt<< Your message has been sent.',
	    '>>es<< He\'s two years older than me.',
	]
	__SCREAMING_SNAKE_CASE              =					[
	    '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       ):
		_lowercase   =TranslationPipeline(self.model       ,  self.tokenizer       ,  framework="tf"       )
		_lowercase   =pipeline(self.src_text       )
		self.assertEqual(self.expected_text       ,  [x["translation_text"] for x in output]       )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




