





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


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case = None       ,      snake_case = None       ,      snake_case=None       ,      snake_case=None   )					->	str:
      """simple docstring"""





      if not conversation_id:
         a__						:    Optional[int]   		=	uuid.uuida()
      if past_user_inputs is None:
         a__						:    List[Any]   		=	[]
      if generated_responses is None:
         a__						:    Dict   		=	[]

      a__						:    uuid.UUID   		=	conversation_id
      a__						:    List[str]   		=	past_user_inputs
      a__						:    List[str]   		=	generated_responses
      a__						:    Optional[str]   		=	text





   def __eq__(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if not isinstance(snake_case       ,      snake_case   ):
         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 						_snake_case			(			self       ,      snake_case       ,      snake_case = False   )					->	Tuple:
      """simple docstring"""





      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}\"."""   )
            a__						:    Optional[Any]   		=	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:
         a__						:    Tuple   		=	text





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input   )
      a__						:    List[Any]   		=	None





   def 						_snake_case			(			self       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      self.generated_responses.append(snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      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   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Any   		=	F"""Conversation id: {self.uuid} \n"""
      for is_user, text in self.iter_texts():
         a__						:    Dict   		=	"user" if is_user else "bot"
         output += F"""{name} >> {text} \n"""
      return output







@add_end_docstrings(
    _UpperCamelCase		,r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """		,)
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   def __init__(			self       ,      *snake_case       ,      **snake_case   )					->	Tuple:
      """simple docstring"""





      super().__init__(*snake_case       ,      **snake_case   )
      if self.tokenizer.pad_token_id is None:
         a__						:    str   		=	self.tokenizer.eos_token





   def 						_snake_case			(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      **snake_case   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	{}
      a__						:    str   		=	{}
      a__						:    Dict   		=	{}

      if min_length_for_response is not None:
         a__						:    int   		=	min_length_for_response
      if minimum_tokens is not None:
         a__						:    List[str]   		=	minimum_tokens

      if "max_length" in generate_kwargs:
         a__						:    Any   		=	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:
         a__						:    List[str]   		=	clean_up_tokenization_spaces

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





   def __call__(			self       ,      snake_case       ,      snake_case=0       ,      **snake_case   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Optional[int]   		=	super().__call__(snake_case       ,      num_workers=snake_case       ,      **snake_case   )
      if isinstance(snake_case       ,      snake_case   ) and len(snake_case   ) == 1:
         return outputs[0]
      return outputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=32   )					->	Dict[str, Any]:
      """simple docstring"""





      if not isinstance(snake_case       ,      snake_case   ):
         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"   ):
         a__						:    Any   		=	self.tokenizer._build_conversation_input_ids(snake_case   )
      else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         a__						:    Union[str, Any]   		=	self._legacy_parse_and_tokenize(snake_case   )

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=10       ,      **snake_case   )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	generate_kwargs.get("max_length"       ,      self.model.config.max_length   )

      a__						:    Tuple   		=	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})"""   )
         a__						:    Any   		=	max_length - minimum_tokens
         a__						:    int   		=	model_inputs["input_ids"][:, -trim:]
         if "attention_mask" in model_inputs:
            a__						:    Optional[Any]   		=	model_inputs["attention_mask"][:, -trim:]
      a__						:    Optional[Any]   		=	model_inputs.pop("conversation"   )
      a__						:    Dict   		=	max_length
      a__						:    Any   		=	self.model.generate(**snake_case       ,      **snake_case   )
      if self.model.config.is_encoder_decoder:
         a__						:    Union[str, Any]   		=	1
      else:
         a__						:    Optional[int]   		=	n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=True   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Any   		=	model_outputs["output_ids"]
      a__						:    Tuple   		=	self.tokenizer.decode(
          output_ids[0]       ,      skip_special_tokens=snake_case       ,      clean_up_tokenization_spaces=snake_case       ,      )
      a__						:    Optional[Any]   		=	model_outputs["conversation"]
      conversation.mark_processed()
      conversation.append_response(snake_case   )
      return conversation





   def 						_snake_case			(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      a__						:    str   		=	self.tokenizer.eos_token_id
      a__						:    Dict   		=	[]
      for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
            input_ids.extend(self.tokenizer.encode(snake_case       ,      add_special_tokens=snake_case   ) + [eos_token_id]   )
         else:
            input_ids.extend(self.tokenizer.encode(snake_case       ,      add_special_tokens=snake_case   )   )

      if len(snake_case   ) > self.tokenizer.model_max_length:
         a__						:    Optional[int]   		=	input_ids[-self.tokenizer.model_max_length :]
      return input_ids








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__							:      List[str]         =    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE__							:      Any         =    """zero2"""
SCREAMING_SNAKE_CASE__							:      Dict         =    """zero3"""
SCREAMING_SNAKE_CASE__							:      str         =    [ZEROa, ZEROa]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   a__						:    Optional[Any]   		=	parameterized.to_safe_name("_".join(str(lowerCamelCase ) for x in param.args ) )
   return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	str:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = True       ,      snake_case = True       ,      snake_case = True       ,      )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	models[model]

      a__						:    int   		=	self.run_trainer(
          stage=snake_case       ,      model_name=snake_case       ,      eval_steps=snake_case       ,      num_train_epochs=1       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )

      self.do_checks(snake_case   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = 1       ,      snake_case = True       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.get_auto_remove_tmp_dir("./xxx"       ,      after=snake_case   )
      a__						:    List[Any]   		=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

      if fpaa:
         args.extend(["--fp16"]   )

      # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
      # hence the separate config files
      a__						:    str   		=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
      a__						:    Dict   		=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
      a__						:    Optional[int]   		=	self.get_launcher(snake_case   )

      a__						:    Optional[int]   		=	launcher + script + args + ds_args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case       ,      env=self.get_env()   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	min(2       ,      get_gpu_count()   ) if distributed else 1
      return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

