


import math

import flax.linen as nn
import jax.numpy as jnp
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = 1							,UpperCamelCase_ = 1							,UpperCamelCase_ = 1.0e4							,UpperCamelCase_ = False							,UpperCamelCase_ = 1.0							,):



     """simple docstring"""






     assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
     assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
     snake_case					=    float(embedding_dim // 2    )
     snake_case					=    math.log(max_timescale / min_timescale    ) / (num_timescales - freq_shift)
     snake_case					=    min_timescale * jnp.exp(jnp.arange(UpperCamelCase_							,dtype=jnp.floataa    ) * -log_timescale_increment    )
     snake_case					=    jnp.expand_dims(UpperCamelCase_							,1    ) * jnp.expand_dims(UpperCamelCase_							,0    )

     # scale embeddings
     snake_case					=    scale * emb

     if flip_sin_to_cos:
          snake_case					=    jnp.concatenate([jnp.cos(UpperCamelCase_    ), jnp.sin(UpperCamelCase_    )]							,axis=1    )
     else:
          snake_case					=    jnp.concatenate([jnp.sin(UpperCamelCase_    ), jnp.cos(UpperCamelCase_    )]							,axis=1    )
     snake_case					=    jnp.reshape(UpperCamelCase_							,[jnp.shape(UpperCamelCase_    )[0], embedding_dim]    )
     return signal

class   A__ (   nn.Module     ):







    """simple docstring"""

    __magic_name__        =		32
    __magic_name__        =		jnp.floataa
    @nn.compact
    def __call__(	self      , __snake_case   ):
         snake_case					=    nn.Dense(self.time_embed_dim      , dtype=self.dtype      , name='''linear_1'''   )(__snake_case   )
         snake_case					=    nn.silu(__snake_case   )
         snake_case					=    nn.Dense(self.time_embed_dim      , dtype=self.dtype      , name='''linear_2'''   )(__snake_case   )
         return temb






class   A__ (   nn.Module     ):







    """simple docstring"""

    __magic_name__        =		32
    __magic_name__        =		False
    __magic_name__        =		1
    @nn.compact
    def __call__(	self      , __snake_case   ):
         return get_sinusoidal_embeddings(
             __snake_case      , embedding_dim=self.dim      , flip_sin_to_cos=self.flip_sin_to_cos      , freq_shift=self.freq_shift   )








import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

class   A__ (   enum.Enum     ):







    """simple docstring"""

    __magic_name__        =		0
    __magic_name__        =		1






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'generated'
    def __init__(	self      , *__snake_case      , **__snake_case   ):
         super().__init__(*__snake_case      , **__snake_case   )

         self.check_model_type(
             TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
             if self.framework == '''tf'''
             else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , **__snake_case      , ):
         snake_case					=    {}
         if truncation is not None:
              snake_case					=    truncation

         snake_case					=    generate_kwargs

         snake_case					=    {}
         if return_tensors is not None and return_type is None:
              snake_case					=    ReturnType.TENSORS if return_tensors else ReturnType.TEXT
         if return_type is not None:
              snake_case					=    return_type

         if clean_up_tokenization_spaces is not None:
              snake_case					=    clean_up_tokenization_spaces

         if stop_sequence is not None:
              snake_case					=    self.tokenizer.encode(__snake_case      , add_special_tokens=__snake_case   )
              if len(__snake_case   ) > 1:
                   warnings.warn(
                       '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                       ''' the stop sequence will be used as the stop sequence string in the interim.'''   )
              snake_case					=    stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         return True
    def 		a_      (	self      , *__snake_case      , __snake_case   ):
         snake_case					=    self.model.config.prefix if self.model.config.prefix is not None else ''''''
         if isinstance(args[0]      , __snake_case   ):
              if self.tokenizer.pad_token_id is None:
                   raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''   )
              snake_case					=    ([prefix + arg for arg in args[0]],)
              snake_case					=    True

         elif isinstance(args[0]      , __snake_case   ):
              snake_case					=    (prefix + args[0],)
              snake_case					=    False
         else:
              raise ValueError(
                  F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
         snake_case					=    self.tokenizer(*__snake_case      , padding=__snake_case      , truncation=__snake_case      , return_tensors=self.framework   )
         # This is produced by tokenizers but is an invalid generate kwargs
         if "token_type_ids" in inputs:
              del inputs["token_type_ids"]
         return inputs
    def __call__(	self      , *__snake_case      , **__snake_case   ):
         snake_case					=    super().__call__(*__snake_case      , **__snake_case   )
         if (
             isinstance(args[0]      , __snake_case   )
             and all(isinstance(__snake_case      , __snake_case   ) for el in args[0]   )
             and all(len(__snake_case   ) == 1 for res in result   )
         ):
              return [res[0] for res in result]
         return result
    def 		a_      (	self      , __snake_case      , __snake_case=TruncationStrategy.DO_NOT_TRUNCATE      , **__snake_case   ):
         snake_case					=    self._parse_and_tokenize(__snake_case      , truncation=__snake_case      , **__snake_case   )
         return inputs
    def 		a_      (	self      , __snake_case      , **__snake_case   ):
         if self.framework == "pt":
              snake_case			,  snake_case					=    model_inputs['''input_ids'''].shape
         elif self.framework == "tf":
              snake_case			,  snake_case					=    tf.shape(model_inputs['''input_ids''']   ).numpy()

         snake_case					=    generate_kwargs.get('''min_length'''      , self.model.config.min_length   )
         snake_case					=    generate_kwargs.get('''max_length'''      , self.model.config.max_length   )
         self.check_inputs(__snake_case      , generate_kwargs['''min_length''']      , generate_kwargs['''max_length''']   )
         snake_case					=    self.model.generate(**__snake_case      , **__snake_case   )
         snake_case					=    output_ids.shape[0]
         if self.framework == "pt":
              snake_case					=    output_ids.reshape(__snake_case      , out_b // in_b      , *output_ids.shape[1:]   )
         elif self.framework == "tf":
              snake_case					=    tf.reshape(__snake_case      , (in_b, out_b // in_b, *output_ids.shape[1:])   )
         return {"output_ids": output_ids}




    def 		a_      (	self      , __snake_case      , __snake_case=ReturnType.TEXT      , __snake_case=False   ):
         snake_case					=    []
         for output_ids in model_outputs["output_ids"][0]:
              if return_type == ReturnType.TENSORS:
                   snake_case					=    {F'''{self.return_name}_token_ids''': output_ids}
              elif return_type == ReturnType.TEXT:
                   snake_case					=    {
                       F'''{self.return_name}_text''': self.tokenizer.decode(
                           __snake_case      , skip_special_tokens=__snake_case      , clean_up_tokenization_spaces=__snake_case      , )
                   }
              records.append(__snake_case   )
         return records






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'summary'
    def __call__(	self      , *__snake_case      , **__snake_case   ):
         return super().__call__(*__snake_case      , **__snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         if max_length < min_length:
              logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''   )

         if input_length < max_length:
              logger.warning(
                  F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                  '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
                  F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''   )






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'translation'
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         if input_length > 0.9 * max_length:
              logger.warning(
                  F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                  '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''   )
         return True
    def 		a_      (	self      , *__snake_case      , __snake_case=TruncationStrategy.DO_NOT_TRUNCATE      , __snake_case=None      , __snake_case=None   ):
         if getattr(self.tokenizer      , '''_build_translation_inputs'''      , __snake_case   ):
              return self.tokenizer._build_translation_inputs(
                  *__snake_case      , return_tensors=self.framework      , truncation=__snake_case      , src_lang=__snake_case      , tgt_lang=__snake_case   )
         else:
              return super()._parse_and_tokenize(*__snake_case      , truncation=__snake_case   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None      , **__snake_case   ):
         snake_case			,  snake_case			,  snake_case					=    super()._sanitize_parameters(**__snake_case   )
         if src_lang is not None:
              snake_case					=    src_lang
         if tgt_lang is not None:
              snake_case					=    tgt_lang
         if src_lang is None and tgt_lang is None:
              # Backward compatibility, direct arguments use is preferred.
              snake_case					=    kwargs.get('''task'''      , self.task   )
              snake_case					=    task.split('''_'''   )
              if task and len(__snake_case   ) == 4:
                   # translation, XX, to YY
                   snake_case					=    items[1]
                   snake_case					=    items[3]
         return preprocess_params, forward_params, postprocess_params




    def __call__(	self      , *__snake_case      , **__snake_case   ):
         return super().__call__(*__snake_case      , **__snake_case   )




