
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		:
     UpperCAmelCase__    :							str									=		BlenderbotConfig
     UpperCAmelCase__    :							Dict									=		{}
     UpperCAmelCase__    :							Tuple									=		"gelu"





     def __init__(self				:   int    ,   _A				:   int    ,   _A				:   Any=1_3    ,   _A				:   Optional[int]=7    ,   _A				:   str=True    ,   _A				:   Union[str, Any]=False    ,   _A				:   str=9_9    ,   _A				:   Optional[int]=3_2    ,   _A				:   Any=2    ,   _A				:   int=4    ,   _A				:   str=3_7    ,   _A				:   str=0.1    ,   _A				:   Any=0.1    ,   _A				:   Dict=2_0    ,   _A				:   Optional[Any]=2    ,   _A				:   Tuple=1    ,   _A				:   int=0    ,   )						->						Optional[int]:
         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 		UpperCAmelCase(self				:   Dict       )						->						List[str]:
         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_inputs_dict(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase       )
         return config, inputs_dict





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   Union[str, Any]    ,   _A				:   str       )						->						Optional[int]:
         snake_case								= TFBlenderbotModel(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								= 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 						lowercase_      (       A__			,		A__			,		A__			,		A__=None			,		A__=None			,		A__=None			,		A__=None			,		A__=None			,		):
    """simple docstring"""

    if attention_mask is None:
        snake_case								= tf.cast(tf.math.not_equal(__UpperCAmelCase			,		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 					lowerCamelCase		(  _UpperCAmelCase							,			_UpperCAmelCase							,			unittest.TestCase  ):
     UpperCAmelCase__    :							str									=		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     UpperCAmelCase__    :							Dict									=		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     UpperCAmelCase__    :							Optional[Any]									=		(
         {
             "conversational": TFBlenderbotForConditionalGeneration,
             "feature-extraction": TFBlenderbotModel,
             "summarization": TFBlenderbotForConditionalGeneration,
             "text2text-generation": TFBlenderbotForConditionalGeneration,
             "translation": TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__    :							List[str]									=		True
     UpperCAmelCase__    :							Dict									=		False
     UpperCAmelCase__    :							Optional[Any]									=		False





     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         snake_case								= TFBlenderbotModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_UpperCAmelCase       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						Optional[Any]:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Tuple:
         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 					lowerCamelCase		(  unittest.TestCase  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["My friends are cool but they eat too many carbs."]
     UpperCAmelCase__    :							str									=		"facebook/blenderbot-400M-distill"





     @cached_property
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         return BlenderbotTokenizer.from_pretrained(self.model_name       )





     @cached_property
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         snake_case								= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
         return model





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						Dict:
         snake_case								= self.tokenizer(self.src_text    ,   return_tensors="tf"       )
         snake_case								= self.model.generate(
             model_inputs.input_ids    ,   )
         snake_case								= self.tokenizer.batch_decode(generated_ids.numpy()    ,   skip_special_tokens=_UpperCAmelCase       )[0]
         assert (
             generated_words
             == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
         )



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		["image_processor", "tokenizer"]
     UpperCAmelCase__    :							Dict									=		"LayoutLMv2ImageProcessor"
     UpperCAmelCase__    :							Optional[Any]									=		("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")





     def __init__(self				:   str    ,   _A				:   Any=None    ,   _A				:   Tuple=None    ,   **_A				:   Optional[Any]       )						->						Optional[int]:
         if "feature_extractor" in kwargs:
             warnings.warn(
                 "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                 " instead."    ,   _A    ,   )
             snake_case								= kwargs.pop("feature_extractor"       )

         snake_case								= image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`."       )
         if tokenizer is None:
             raise ValueError("You need to specify a `tokenizer`."       )

         super().__init__(_A    ,   _A       )





     def __call__(self				:   int    ,   _A				:   List[str]    ,   _A				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   _A				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,   _A				:   Union[List[List[int]], List[List[List[int]]]] = None    ,   _A				:   Optional[Union[List[int], List[List[int]]]] = None    ,   _A				:   bool = True    ,   _A				:   Union[bool, str, PaddingStrategy] = False    ,   _A				:   Union[bool, str, TruncationStrategy] = None    ,   _A				:   Optional[int] = None    ,   _A				:   int = 0    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = True    ,   _A				:   Optional[Union[str, TensorType]] = None    ,   **_A				:   Dict    ,   )						->						BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 "You cannot provide bounding boxes "
                 "if you initialized the image processor with apply_ocr set to True."       )

         if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."       )

         # first, apply the image processor
         snake_case								= self.image_processor(images=_A    ,   return_tensors=_A       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(_A    ,   _A       ):
                 snake_case								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             snake_case								= features["words"]

         snake_case								= self.tokenizer(
             text=text if text is not None else features["words"]    ,   text_pair=text_pair if text_pair is not None else None    ,   boxes=boxes if boxes is not None else features["boxes"]    ,   word_labels=_A    ,   add_special_tokens=_A    ,   padding=_A    ,   truncation=_A    ,   max_length=_A    ,   stride=_A    ,   pad_to_multiple_of=_A    ,   return_token_type_ids=_A    ,   return_attention_mask=_A    ,   return_overflowing_tokens=_A    ,   return_special_tokens_mask=_A    ,   return_offsets_mapping=_A    ,   return_length=_A    ,   verbose=_A    ,   return_tensors=_A    ,   **_A    ,   )

         # add pixel values
         snake_case								= features.pop("pixel_values"       )
         if return_overflowing_tokens is True:
             snake_case								= self.get_overflowing_images(_A    ,   encoded_inputs["overflow_to_sample_mapping"]       )
         snake_case								= images

         return encoded_inputs





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Dict    ,   _A				:   List[str]       )						->						Optional[int]:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         snake_case								= []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]       )

         if len(_A       ) != len(_A       ):
             raise ValueError(
                 "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                 f' {len(_A       )} and {len(_A       )}'       )

         return images_with_overflow





     def 		UpperCAmelCase(self				:   Tuple    ,   *_A				:   int    ,   **_A				:   Dict       )						->						str:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   str    ,   *_A				:   List[Any]    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         return ["input_ids", "bbox", "attention_mask", "image"]





     @property
     def 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,   _A    ,   )
         return self.image_processor_class





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   _A    ,   )
         return self.image_processor

