"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase=None   ,lowercase=None		):







  """simple docstring"""



  if attention_mask is None:
    _UpperCAmelCase						 =      tf.cast(tf.math.not_equal(lowercase   ,config.pad_token_id		)   ,tf.inta		)
  return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class        a				:
    _snake_case     :  Dict     							=							OPTConfig
    _snake_case     :  Union[str, Any]     							=							{}
    _snake_case     :  Dict     							=							'gelu'


    def __init__(					self	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Union[str, Any]=13   ,				__lowerCAmelCase	:  List[str]=7   ,				__lowerCAmelCase	:  List[str]=True   ,				__lowerCAmelCase	:  List[str]=False   ,				__lowerCAmelCase	:  Union[str, Any]=99   ,				__lowerCAmelCase	:  Optional[Any]=16   ,				__lowerCAmelCase	:  List[Any]=2   ,				__lowerCAmelCase	:  List[str]=4   ,				__lowerCAmelCase	:  Dict=4   ,				__lowerCAmelCase	:  Union[str, Any]="gelu"   ,				__lowerCAmelCase	:  List[Any]=0.1   ,				__lowerCAmelCase	:  str=0.1   ,				__lowerCAmelCase	:  Any=20   ,				__lowerCAmelCase	:  Tuple=2   ,				__lowerCAmelCase	:  Dict=1   ,				__lowerCAmelCase	:  List[str]=0   ,				__lowerCAmelCase	:  str=16   ,				__lowerCAmelCase	:  List[str]=16   ,				):
      _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_act
      _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
      _UpperCAmelCase						 =      embed_dim
      _UpperCAmelCase						 =      word_embed_proj_dim
      _UpperCAmelCase						 =      False


    def      lowerCAmelCase_  (					self	:  int					):
      _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						 =      self.config_cls(
          vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				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_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				embed_dim=self.embed_dim   ,				word_embed_proj_dim=self.word_embed_proj_dim   ,				is_encoder_decoder=snake_case__   ,				**self.config_updates   ,				)
      _UpperCAmelCase						 =      prepare_opt_inputs_dict(snake_case__   ,				snake_case__					)
      return config, inputs_dict


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TFOPTModel(config=snake_case__					)
      _UpperCAmelCase						 =      inputs_dict["input_ids"]

      _UpperCAmelCase						 =      input_ids[:1, :]
      _UpperCAmelCase						 =      inputs_dict["attention_mask"][:1, :]
      _UpperCAmelCase						 =      1

      # first forward pass
      _UpperCAmelCase						 =      model(snake_case__   ,				attention_mask=snake_case__   ,				use_cache=snake_case__					)

      _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					)



@require_tf
class        a				(		SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							unittest.TestCase   ):
    _snake_case     :  Tuple     							=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
    _snake_case     :  Optional[Any]     							=							(TFOPTForCausalLM,) if is_tf_available() else ()
    _snake_case     :  Optional[int]     							=							(
        {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
    )
    _snake_case     :  str     							=							False
    _snake_case     :  Tuple     							=							False
    _snake_case     :  str     							=							False
    _snake_case     :  Union[str, Any]     							=							10


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      TFOPTModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=snake_case__					)


    def      lowerCAmelCase_  (					self	:  str					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*snake_case__					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]					):
        if hasattr(snake_case__   ,				"""weight"""					):
          return embedding_layer.weight
        else:
          # Here we build the word embeddings weights if not exists.
          # And then we retry to get the attribute once built.
          model.build()
          if hasattr(snake_case__   ,				"""weight"""					):
            return embedding_layer.weight
          else:
            return None

      for model_class in self.all_model_classes:
        for size in [config.vocab_size - 10, config.vocab_size + 10]:
          # build the embeddings
          _UpperCAmelCase						 =      model_class(config=snake_case__					)
          _UpperCAmelCase						 =      _get_word_embedding_weight(snake_case__   ,				model.get_input_embeddings()					)
          _UpperCAmelCase						 =      _get_word_embedding_weight(snake_case__   ,				model.get_output_embeddings()					)

          # reshape the embeddings
          model.resize_token_embeddings(snake_case__					)
          _UpperCAmelCase						 =      _get_word_embedding_weight(snake_case__   ,				model.get_input_embeddings()					)
          _UpperCAmelCase						 =      _get_word_embedding_weight(snake_case__   ,				model.get_output_embeddings()					)

          # check that the resized embeddings size matches the desired size.
          _UpperCAmelCase						 =      size if size is not None else config.vocab_size

          self.assertEqual(new_input_embeddings.shape[0]   ,				snake_case__					)

          # check that weights remain the same after resizing
          _UpperCAmelCase						 =      True
          for pa, pa in zip(old_input_embeddings.value()   ,				new_input_embeddings.value()					):
            if tf.math.reduce_sum(tf.math.abs(pa - pa					)					) > 0:
              _UpperCAmelCase						 =      False
          self.assertTrue(snake_case__					)

          if old_output_embeddings is not None and new_output_embeddings is not None:
            self.assertEqual(new_output_embeddings.shape[0]   ,				snake_case__					)

            _UpperCAmelCase						 =      True
            for pa, pa in zip(old_output_embeddings.value()   ,				new_output_embeddings.value()					):
              if tf.math.reduce_sum(tf.math.abs(pa - pa					)					) > 0:
                _UpperCAmelCase						 =      False
            self.assertTrue(snake_case__					)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return tf.constant(lowercase   ,dtype=tf.intaa		)


@require_tf
class        a				(		unittest.TestCase   ):
    _snake_case     :  Dict     							=							99


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      tf.ones((4, 1)   ,				dtype=tf.intaa					) * 2
      _UpperCAmelCase						 =      tf.concat([ids_tensor((4, 6)   ,				self.vocab_size - 3					) + 3, eos_column_vector]   ,				axis=1					)
      _UpperCAmelCase						 =      input_ids.shape[0]
      _UpperCAmelCase						 =      OPTConfig(
          vocab_size=self.vocab_size   ,				hidden_size=24   ,				num_hidden_layers=2   ,				num_attention_heads=2   ,				ffn_dim=32   ,				max_position_embeddings=48   ,				eos_token_id=2   ,				pad_token_id=1   ,				bos_token_id=0   ,				)
      return config, input_ids, batch_size



@require_sentencepiece
@require_tf
class        a				(		unittest.TestCase   ):


    @slow
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      TFOPTModel.from_pretrained("""facebook/opt-350m"""					)
      _UpperCAmelCase						 =      _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
      _UpperCAmelCase						 =      tf.not_equal(snake_case__   ,				model.config.pad_token_id					)
      with tf.GradientTape():
        _UpperCAmelCase						 =      model(input_ids=snake_case__   ,				attention_mask=snake_case__					).last_hidden_state
      _UpperCAmelCase						 =      (1, 11, 512)
      self.assertEqual(output.shape   ,				snake_case__					)
      _UpperCAmelCase						 =      tf.constant(
          [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]					)
      self.assertTrue(np.allclose(output[:, :3, :3]   ,				snake_case__   ,				atol=4e-3					)					)

      _UpperCAmelCase						 =      tf.function(snake_case__   ,				jit_compile=snake_case__					)
      _UpperCAmelCase						 =      xla_generate(snake_case__   ,				snake_case__					)[0]
      self.assertTrue(np.allclose(output[:, :3, :3]   ,				snake_case__   ,				atol=4e-2					)					)



@require_tf
@slow
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      super().setUp()
      _UpperCAmelCase						 =      "facebook/opt-350m"


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      TFOPTForCausalLM.from_pretrained(self.path_model					)
      _UpperCAmelCase						 =      GPTaTokenizer.from_pretrained(self.path_model					)

      _UpperCAmelCase						 =      [
          "Today is a beautiful day and I want to",
          "In the city of",
          "Paris is the capital of France and",
          "Computers and mobile phones have taken",
      ]
      # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
      _UpperCAmelCase						 =      tokenizer(snake_case__   ,				return_tensors="""tf"""   ,				padding=snake_case__   ,				add_special_tokens=snake_case__					)
      _UpperCAmelCase						 =      tf.math.reduce_mean(model(inputs.input_ids   ,				attention_mask=inputs.attention_mask					)[0]   ,				axis=-1					)
      _UpperCAmelCase						 =      tf.constant(
          [
              [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
              [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
              [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
              [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
          ]					)
      self.assertTrue(np.allclose(snake_case__   ,				snake_case__   ,				atol=1e-4					)					)

      _UpperCAmelCase						 =      tf.function(snake_case__   ,				jit_compile=snake_case__					)
      _UpperCAmelCase						 =      tf.math.reduce_mean(xla_generate(inputs.input_ids   ,				attention_mask=inputs.attention_mask					)[0]   ,				axis=-1					)
      self.assertTrue(np.allclose(snake_case__   ,				snake_case__   ,				atol=1e-4					)					)



@require_tf
@slow
class        a				(		unittest.TestCase   ):


    @property
    def      lowerCAmelCase_  (					self	:  str					):
      return [
          "Today is a beautiful day and I want",
          "In the city of",
          "Paris is the capital of France and",
          "Computers and mobile phones have taken",
      ]


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      "facebook/opt-125m"

      _UpperCAmelCase						 =      [
          "Today is a beautiful day and I want to",
          "In the city of New York, the city",
          "Paris is the capital of France and the capital",
          "Computers and mobile phones have taken over the",
      ]

      _UpperCAmelCase						 =      []
      _UpperCAmelCase						 =      GPTaTokenizer.from_pretrained(snake_case__					)
      _UpperCAmelCase						 =      TFOPTForCausalLM.from_pretrained(snake_case__					)

      for prompt in self.prompts:
        _UpperCAmelCase						 =      tokenizer(snake_case__   ,				return_tensors="""tf"""					).input_ids

        _UpperCAmelCase						 =      model.generate(snake_case__   ,				max_length=10					)

        _UpperCAmelCase						 =      tokenizer.batch_decode(snake_case__   ,				skip_special_tokens=snake_case__					)
        predicted_outputs += generated_string

      self.assertListEqual(snake_case__   ,				snake_case__					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      "facebook/opt-350m"

      _UpperCAmelCase						 =      GPTaTokenizer.from_pretrained(snake_case__					)
      _UpperCAmelCase						 =      TFOPTForCausalLM.from_pretrained(snake_case__					)

      _UpperCAmelCase						 =      "left"

      # use different length sentences to test batching
      _UpperCAmelCase						 =      [
          "Hello, my dog is a little",
          "Today, I",
      ]

      _UpperCAmelCase						 =      tokenizer(snake_case__   ,				return_tensors="""tf"""   ,				padding=snake_case__					)
      _UpperCAmelCase						 =      inputs["input_ids"]

      _UpperCAmelCase						 =      model.generate(input_ids=snake_case__   ,				attention_mask=inputs["""attention_mask"""]					)

      _UpperCAmelCase						 =      tokenizer(sentences[0]   ,				return_tensors="""tf"""					).input_ids
      _UpperCAmelCase						 =      model.generate(input_ids=snake_case__					)

      _UpperCAmelCase						 =      inputs_non_padded.shape[-1] - tf.math.reduce_sum(
          tf.cast(inputs["""attention_mask"""][-1]   ,				tf.intaa					)					)
      _UpperCAmelCase						 =      tokenizer(sentences[1]   ,				return_tensors="""tf"""					).input_ids
      _UpperCAmelCase						 =      model.generate(input_ids=snake_case__   ,				max_length=model.config.max_length - num_paddings					)

      _UpperCAmelCase						 =      tokenizer.batch_decode(snake_case__   ,				skip_special_tokens=snake_case__					)
      _UpperCAmelCase						 =      tokenizer.decode(output_non_padded[0]   ,				skip_special_tokens=snake_case__					)
      _UpperCAmelCase						 =      tokenizer.decode(output_padded[0]   ,				skip_special_tokens=snake_case__					)

      _UpperCAmelCase						 =      [
          "Hello, my dog is a little bit of a dork.\nI'm a little bit",
          "Today, I was in the middle of a conversation with a friend about the",
      ]
      self.assertListEqual(snake_case__   ,				snake_case__					)
      self.assertListEqual(snake_case__   ,				[non_padded_sentence, padded_sentence]					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      "facebook/opt-350m"

      _UpperCAmelCase						 =      [
          "Today is a beautiful day and I want to",
          "In the city of San Francisco, the city",
          "Paris is the capital of France and the capital",
          "Computers and mobile phones have taken over the",
      ]

      _UpperCAmelCase						 =      []
      _UpperCAmelCase						 =      GPTaTokenizer.from_pretrained(snake_case__					)
      _UpperCAmelCase						 =      TFOPTForCausalLM.from_pretrained(snake_case__					)

      for prompt in self.prompts:
        _UpperCAmelCase						 =      tokenizer(snake_case__   ,				return_tensors="""tf"""					).input_ids

        _UpperCAmelCase						 =      model.generate(snake_case__   ,				max_length=10					)

        _UpperCAmelCase						 =      tokenizer.batch_decode(snake_case__   ,				skip_special_tokens=snake_case__					)
        predicted_outputs += generated_string

      self.assertListEqual(snake_case__   ,				snake_case__					)







"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  uuid.UUID = None   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Tuple=None					):
      if not conversation_id:
        _UpperCAmelCase						 =      uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase						 =      []
      if generated_responses is None:
        _UpperCAmelCase						 =      []

      _UpperCAmelCase						 =      conversation_id
      _UpperCAmelCase						 =      past_user_inputs
      _UpperCAmelCase						 =      generated_responses
      _UpperCAmelCase						 =      text


    def __eq__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  bool = False					):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''					)
          _UpperCAmelCase						 =      text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
      else:
        _UpperCAmelCase						 =      text


    def      lowerCAmelCase_  (					self	:  int					):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      self.generated_responses.append(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses					):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(					self	:  Optional[int]					):
      _UpperCAmelCase						 =      f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase						 =      """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_	,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,							)
class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  List[Any]   ,				**__lowerCAmelCase	:  Tuple					):
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase						 =      self.tokenizer.eos_token


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}

      if min_length_for_response is not None:
        _UpperCAmelCase						 =      min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase						 =      minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase						 =      generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase						 =      clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase					)
      return preprocess_params, forward_params, postprocess_params


    def __call__(					self	:  Tuple   ,				__lowerCAmelCase	:  Union[Conversation, List[Conversation]]   ,				__lowerCAmelCase	:  int=0   ,				**__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      super().__call__(__lowerCAmelCase   ,				num_workers=__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
        return outputs[0]
      return outputs


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Conversation   ,				__lowerCAmelCase	:  Optional[int]=32					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""					)
      if hasattr(self.tokenizer   ,				"""_build_conversation_input_ids"""					):
        _UpperCAmelCase						 =      self.tokenizer._build_conversation_input_ids(__lowerCAmelCase					)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase						 =      self._legacy_parse_and_tokenize(__lowerCAmelCase					)

      if self.framework == "pt":
        _UpperCAmelCase						 =      torch.LongTensor([input_ids]					)
      elif self.framework == "tf":
        _UpperCAmelCase						 =      tf.constant([input_ids]					)
      return {"input_ids": input_ids, "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]=10   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      generate_kwargs.get("""max_length"""   ,				self.model.config.max_length					)

      _UpperCAmelCase						 =      model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
        _UpperCAmelCase						 =      max_length - minimum_tokens
        _UpperCAmelCase						 =      model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase						 =      model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase						 =      model_inputs.pop("""conversation"""					)
      _UpperCAmelCase						 =      max_length
      _UpperCAmelCase						 =      self.model.generate(**__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase						 =      1
      else:
        _UpperCAmelCase						 =      n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]=True					):
      _UpperCAmelCase						 =      model_outputs["""output_ids"""]
      _UpperCAmelCase						 =      self.tokenizer.decode(
          output_ids[0]   ,				skip_special_tokens=__lowerCAmelCase   ,				clean_up_tokenization_spaces=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase					)
      return conversation


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Conversation					):
      _UpperCAmelCase						 =      self.tokenizer.eos_token_id
      _UpperCAmelCase						 =      []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [eos_token_id]					)
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					)					)

      if len(__lowerCAmelCase					) > self.tokenizer.model_max_length:
        _UpperCAmelCase						 =      input_ids[-self.tokenizer.model_max_length :]
      return input_ids
