
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


a__				:      Any    		=     -1 if torch_device == '''cpu''' else 0






@is_pipeline_test
class     a_       (			unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = SummarizationPipeline(model=_lowerCamelCase				,       tokenizer=_lowerCamelCase   )
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = summarizer.model

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summarizer('''(CNN)The Palestinian Authority officially became'''   )
           self.assertEqual(_lowerCamelCase				,       [{'''summary_text''': ANY(_lowerCamelCase   )}]   )

           SCREAMING_SNAKE_CASE				:				Any				      = summarizer(
               '''(CNN)The Palestinian Authority officially became '''				,       num_beams=2				,       min_length=2				,       max_length=5				,       )
           self.assertEqual(_lowerCamelCase				,       [{'''summary_text''': ANY(_lowerCamelCase   )}]   )

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
               '''SwitchTransformersConfig''',
               '''T5Config''',
               '''LongT5Config''',
               '''LEDConfig''',
               '''PegasusXConfig''',
               '''FSMTConfig''',
               '''M2M100Config''',
               '''ProphetNetConfig''',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
               # Too long and exception is expected.
               # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
               # the embedding layer.
               if not (
                   isinstance(_lowerCamelCase				,       _lowerCamelCase   )
                   and get_gpu_count() > 0
                   and len(summarizer.model.trainable_weights   ) > 0
               ):
                   with self.assertRaises(_lowerCamelCase   ):
                       SCREAMING_SNAKE_CASE				:				Tuple				      = summarizer('''This ''' * 1000   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = summarizer('''This ''' * 1000				,       truncation=TruncationStrategy.ONLY_FIRST   )






       @require_torch
       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				str				      = pipeline(task='''summarization'''				,       model='''sshleifer/tiny-mbart'''				,       framework='''pt'''   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = summarizer('''This is a small test'''   )
           self.assertEqual(
               _lowerCamelCase				,       [
                   {
                       '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
                   }
               ]				,       )






       @require_tf
       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = pipeline(task='''summarization'''				,       model='''sshleifer/tiny-mbart'''				,       framework='''tf'''   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = summarizer('''This is a small test'''   )
           self.assertEqual(
               _lowerCamelCase				,       [
                   {
                       '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
                   }
               ]				,       )






       @require_torch
       @slow
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipeline(task='''summarization'''				,       device=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = (
               ''' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'''
               ''' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'''
               ''' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'''
               ''' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'''
               ''' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'''
               ''' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'''
               ''' situation in Palestinian territories, paving the way for possible war crimes investigations against'''
               ''' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'''
               ''' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'''
               ''' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'''
               ''' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'''
               ''' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'''
               ''' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'''
               ''' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'''
               ''' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'''
               ''' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'''
               ''' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'''
               ''' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'''
               ''' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'''
               ''' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'''
               ''' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'''
               ''' decision to join a treaty to which over 100 countries around the world are members." In January, when'''
               ''' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'''
               ''' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'''
               ''' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'''
               ''' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'''
               ''' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'''
               ''' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'''
               ''' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'''
               ''' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'''
               ''' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'''
               ''' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'''
               ''' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'''
               ''' will include alleged war crimes committed since June. The International Criminal Court was set up in'''
               ''' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'''
               ''' and Faith Karimi contributed to this report.'''
           )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (
               ''' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'''
               ''' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'''
               ''' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'''
               ''' says governments seeking to penalize Palestine should end pressure .'''
           )
           SCREAMING_SNAKE_CASE				:				int				      = summarizer(_lowerCamelCase   )
           self.assertEqual(result[0]['''summary_text''']				,       _lowerCamelCase   )


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


a__				:      Tuple    		=     [nn.LayerNorm]

a__				:      Tuple    		=     logging.get_logger(__name__)

a__				:      int    		=     version.parse(version.parse(torch.__version__).base_version)

a__				:      Dict    		=     parsed_torch_version_base >= version.parse('''2.0''')
a__				:      Any    		=     parsed_torch_version_base >= version.parse('''1.12''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base >= version.parse('''1.11''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base < version.parse('''1.11''')







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						self   ):



    """simple docstring"""



    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(a__ ,						a__ ,						parent.dim ,						self   )
    else:
        return _softmax_backward_data(a__ ,						a__ ,						parent.dim ,						self.dtype   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = 0   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = index.to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = layer.weight.index_select(a__ ,						a__   ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            SCREAMING_SNAKE_CASE				:				int				      = layer.bias.clone().detach()
        else:
            SCREAMING_SNAKE_CASE				:				List[Any]				      = layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE				:				List[Any]				      = list(layer.weight.size()   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = len(a__   )
    SCREAMING_SNAKE_CASE				:				str				      = nn.Linear(new_size[1] ,						new_size[0] ,						bias=layer.bias is not None   ).to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = False
    new_layer.weight.copy_(W.contiguous()   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = True
    if layer.bias is not None:
        SCREAMING_SNAKE_CASE				:				Dict				      = False
        new_layer.bias.copy_(b.contiguous()   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = True
    return new_layer






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->int:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				List[str]				      = nf
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Parameter(torch.empty(_lowerCamelCase				,       _lowerCamelCase   )   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = nn.Parameter(torch.zeros(_lowerCamelCase   )   )
           nn.init.normal_(self.weight				,       std=0.0_2   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = x.size()[:-1] + (self.nf,)
           SCREAMING_SNAKE_CASE				:				int				      = torch.addmm(self.bias				,       x.view(-1				,       x.size(-1   )   )				,       self.weight   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = x.view(_lowerCamelCase   )
           return x







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = 1   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = index.to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Any				      = layer.weight.index_select(a__ ,						a__   ).clone().detach()
    if dim == 0:
        SCREAMING_SNAKE_CASE				:				Tuple				      = layer.bias.clone().detach()
    else:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE				:				int				      = list(layer.weight.size()   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = len(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = ConvaD(new_size[1] ,						new_size[0]   ).to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = False
    new_layer.weight.copy_(W.contiguous()   )
    SCREAMING_SNAKE_CASE				:				int				      = True
    SCREAMING_SNAKE_CASE				:				Any				      = False
    new_layer.bias.copy_(b.contiguous()   )
    SCREAMING_SNAKE_CASE				:				int				      = True
    return new_layer







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = None   ):



    """simple docstring"""



    if isinstance(a__ ,						nn.Linear   ):
        return prune_linear_layer(a__ ,						a__ ,						dim=0 if dim is None else dim   )
    elif isinstance(a__ ,						a__   ):
        return prune_convad_layer(a__ ,						a__ ,						dim=1 if dim is None else dim   )
    else:
        raise ValueError(F"""Can't prune layer of class {layer.__class__}"""   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						*a__   ):



    """simple docstring"""



    assert len(a__   ) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    SCREAMING_SNAKE_CASE				:				str				      = len(inspect.signature(a__   ).parameters   )
    if num_args_in_forward_chunk_fn != len(a__   ):
        raise ValueError(
            F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a__   )} input """
            '''tensors are given'''   )

    if chunk_size > 0:
        SCREAMING_SNAKE_CASE				:				Dict				      = input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    F"""All input tenors have to be of the same shape: {tensor_shape}, """
                    F"""found shape {input_tensor.shape[chunk_dim]}"""   )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                F"""size {chunk_size}"""   )

        SCREAMING_SNAKE_CASE				:				Any				      = input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = tuple(input_tensor.chunk(a__ ,						dim=a__   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        SCREAMING_SNAKE_CASE				:				Tuple				      = tuple(forward_fn(*a__   ) for input_tensors_chunk in zip(*a__   )   )
        # concatenate output at same dimension
        return torch.cat(a__ ,						dim=a__   )

    return forward_fn(*a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.ones(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = set(a__   ) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = head - sum(1 if h < head else 0 for h in already_pruned_heads   )
        SCREAMING_SNAKE_CASE				:				str				      = 0
    SCREAMING_SNAKE_CASE				:				List[str]				      = mask.view(-1   ).contiguous().eq(1   )
    SCREAMING_SNAKE_CASE				:				torch.LongTensor				      = torch.arange(len(a__   )   )[mask].long()
    return heads, index







def 				UpperCAmelCase_(    *a__ ,						a__ = None   ):



    """simple docstring"""



    return torch.meshgrid(*a__ ,						indexing=a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    return tensor.device, storage_ptr(a__   ), storage_size(a__   )
