

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class    A__ :



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__=99						,			UpperCamelCase__=13						,			UpperCamelCase__=16						,			UpperCamelCase__=7						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=False						,			UpperCamelCase__=True						,			UpperCamelCase__=2						,			UpperCamelCase__=32						,			UpperCamelCase__=4						,			UpperCamelCase__=4						,			UpperCamelCase__=30						,			UpperCamelCase__=0						,			UpperCamelCase__=1						,			UpperCamelCase__=2						,			UpperCamelCase__=None						,			)  -> Any:





           '''simple docstring'''
           A_		   = parent
           A_		   = batch_size
           A_		   = decoder_seq_length
           # For common tests
           A_		   = self.decoder_seq_length
           A_		   = is_training
           A_		   = use_attention_mask
           A_		   = use_labels

           A_		   = vocab_size
           A_		   = d_model
           A_		   = d_model
           A_		   = decoder_layers
           A_		   = decoder_layers
           A_		   = decoder_ffn_dim
           A_		   = decoder_attention_heads
           A_		   = decoder_attention_heads
           A_		   = eos_token_id
           A_		   = bos_token_id
           A_		   = pad_token_id
           A_		   = decoder_start_token_id
           A_		   = use_cache
           A_		   = max_position_embeddings

           A_		   = None
           A_		   = decoder_seq_length
           A_		   = 2
           A_		   = 1



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ids_tensor([self.batch_size, self.decoder_seq_length]						,			self.vocab_size    )

           A_		   = None
           if self.use_attention_mask:
                  A_		   = ids_tensor([self.batch_size, self.decoder_seq_length]						,			vocab_size=2    )

           A_		   = None
           if self.use_labels:
                  A_		   = ids_tensor([self.batch_size, self.decoder_seq_length]						,			self.vocab_size    )

           A_		   = TrOCRConfig(
               vocab_size=self.vocab_size						,			d_model=self.d_model						,			decoder_layers=self.decoder_layers						,			decoder_ffn_dim=self.decoder_ffn_dim						,			decoder_attention_heads=self.decoder_attention_heads						,			eos_token_id=self.eos_token_id						,			bos_token_id=self.bos_token_id						,			use_cache=self.use_cache						,			pad_token_id=self.pad_token_id						,			decoder_start_token_id=self.decoder_start_token_id						,			max_position_embeddings=self.max_position_embeddings						,			)

           return (config, input_ids, attention_mask, lm_labels)



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			)  -> Tuple:





           '''simple docstring'''
           A_		   = True
           A_		   = TrOCRDecoder(config=_SCREAMING_SNAKE_CASE    ).to(_SCREAMING_SNAKE_CASE    ).eval()
           A_		   = input_ids[:2]

           input_ids[input_ids == 0] += 1
           # first forward pass
           A_		   = model(_SCREAMING_SNAKE_CASE						,			use_cache=_SCREAMING_SNAKE_CASE    )
           A_		   = model(_SCREAMING_SNAKE_CASE    )
           A_		   = model(_SCREAMING_SNAKE_CASE						,			use_cache=_SCREAMING_SNAKE_CASE    )

           self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE    ) == len(_SCREAMING_SNAKE_CASE    )    )
           self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE    ) == len(_SCREAMING_SNAKE_CASE    ) + 1    )

           A_		   = outputs["""past_key_values"""]

           # create hypothetical next token and extent to next_input_ids
           A_		   = ids_tensor((2, 1)						,			config.vocab_size - 1    ) + 1

           # append to next input_ids and
           A_		   = torch.cat([input_ids, next_tokens]						,			dim=-1    )

           A_		   = model(_SCREAMING_SNAKE_CASE    )["""last_hidden_state"""]
           A_		   = model(_SCREAMING_SNAKE_CASE						,			past_key_values=_SCREAMING_SNAKE_CASE    )["""last_hidden_state"""]

           # select random slice
           A_		   = ids_tensor((1,)						,			output_from_past.shape[-1]    ).item()
           A_		   = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
           A_		   = output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           assert torch.allclose(_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			atol=1e-3    )







    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = self.prepare_config_and_inputs()
           A_		   = config_and_inputs

           A_		   = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict


@require_torch
class    A__ ( UpperCAmelCase__  ,  UpperCAmelCase__  ,  UpperCAmelCase__  ,  unittest.TestCase     ):
    lowercase		   =						(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
    lowercase		   =						(TrOCRForCausalLM,) if is_torch_available() else ()
    lowercase		   =						{'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
    lowercase		   =						True
    lowercase		   =						False



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = TrOCRStandaloneDecoderModelTester(self						,			is_training=_SCREAMING_SNAKE_CASE    )
           A_		   = ConfigTester(self						,			config_class=_SCREAMING_SNAKE_CASE    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_decoder_model_past(*_SCREAMING_SNAKE_CASE    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           return







    @unittest.skip("""The model doesn't support left padding"""    )  # and it's not used enough to be worth fixing :)
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           pass



'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """repo_id""": str(UpperCAmelCase__			),
           """repo_sha""": str(repo.head.object.hexsha			),
           """repo_branch""": str(repo.active_branch			),
       }

       with open(os.path.join(UpperCAmelCase__,    """git_log.json"""			),    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__,    indent=4			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs"""			)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              A_		   = int(os.environ["""WORLD_SIZE"""]			)
              A_		   = int(os.environ["""N_GPU_NODE"""]			)
              A_		   = int(os.environ["""RANK"""]			)

              # number of nodes / node ID
              A_		   = params.world_size // params.n_gpu_per_node
              A_		   = params.global_rank // params.n_gpu_per_node
              A_		   = True

              assert params.n_nodes == int(os.environ["""N_NODES"""]			)
              assert params.node_id == int(os.environ["""NODE_RANK"""]			)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes			)
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id			)
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank			)
       logger.info(PREFIX + """World size     : %i""" % params.world_size			)
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node			)
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master			)			)
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node			)			)
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu			)			)
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()			)

       # set GPU device
       torch.cuda.set_device(params.local_rank			)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed"""			)
              torch.distributed.init_process_group(
                  init_method="""env://""",    backend="""nccl""",    )







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
