



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    _lowercase :
				'''simple docstring'''







				lowercase__     =    MarianConfig
				lowercase__     =    {}
				lowercase__     =    """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  ,			):

									'''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    _lowerCamelCase       (			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_marian_inputs_dict(snake_case__  ,			snake_case__  ,			snake_case__     )
									return config, inputs_dict

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= TFMarianModel(config=snake_case__     ).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(snake_case__  ,			attention_mask=snake_case__  ,			head_mask=snake_case__  ,			use_cache=snake_case__     )

									UpperCamelCase_	,     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(snake_case__  ,			attention_mask=snake_case__     )[0]
									UpperCamelCase_			= 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
									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(snake_case__  ,			snake_case__  ,			rtol=1e-3     )




def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						):
					if attention_mask is None:
										UpperCamelCase_			= tf.cast(tf.math.not_equal(_lowerCAmelCase ,						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    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= TFMarianModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self.config_tester.run_common_tests()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*snake_case__     )




@require_tf
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







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

				@classmethod
				def    _lowerCamelCase       (			cls     ):

									'''simple docstring'''


									UpperCamelCase_			= F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls

				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.tokenizer.eos_token_id

				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									warnings.simplefilter("error"     )
									UpperCamelCase_			= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
									assert isinstance(snake_case__  ,			snake_case__     )
									UpperCamelCase_			= 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__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.translate_src_text(**snake_case__     )
									self.assertListEqual(self.expected_text  ,			snake_case__     )

				def    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


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




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """mt"""
				lowercase__     =    """en"""
				lowercase__     =    ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
				lowercase__     =    ["""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     ):

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """en"""
				lowercase__     =    """zh"""
				lowercase__     =    ["""My name is Wolfgang and I live in Berlin"""]
				lowercase__     =    ["""我叫沃尔夫冈 我住在柏林"""]

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

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """en"""
				lowercase__     =    """ROMANCE"""
				lowercase__     =    [
				    """>>fr<< Don't spend so much time watching TV.""",
				    """>>pt<< Your message has been sent.""",
				    """>>es<< He's two years older than me.""",
				]
				lowercase__     =    [
				    """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     ):

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()

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

									'''simple docstring'''


									UpperCamelCase_			= TranslationPipeline(self.model  ,			self.tokenizer  ,			framework="tf"     )
									UpperCamelCase_			= pipeline(self.src_text     )
									self.assertEqual(self.expected_text  ,			[x["translation_text"] for x in output]     )





'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. 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

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


a  :     Optional[Any]       =       {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							a  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

