




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class     A					(	unittest.TestCase					):
       lowercase_           =       ViTImageProcessor if is_vision_available() else None







       @property
       def      __lowerCAmelCase    (						self :						Dict      )    ->							Any:


              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()







       def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Dict:


              """simple docstring"""


              _a			  =       (3, 32, 1_28)
              _a			  =       tempfile.mkdtemp()

              # fmt: off
              _a			  =       ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
              # fmt: on
              _a			  =       dict(zip(lowerCAmelCase_							,    range(len(lowerCAmelCase_      )      )      )      )

              _a			  =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['''vocab_file''']      )
              with open(self.vocab_file							,    '''w'''							,    encoding='''utf-8'''      ) as fp:
                     fp.write(json.dumps(lowerCAmelCase_      ) + '''\n'''      )

              _a			  =       {
                  '''do_normalize''': False,
                  '''do_resize''': True,
                  '''image_processor_type''': '''ViTImageProcessor''',
                  '''resample''': 3,
                  '''size''': {'''height''': 32, '''width''': 1_28},
              }
              _a			  =       os.path.join(self.tmpdirname							,    lowerCAmelCase_      )
              with open(self.image_processor_file							,    '''w'''							,    encoding='''utf-8'''      ) as fp:
                     json.dump(lowerCAmelCase_							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						str							,    **lowerCAmelCase_ :						int      )    ->							Union[str, Any]:


              """simple docstring"""


              return MgpstrTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						List[str]							,    **lowerCAmelCase_ :						Dict      )    ->							Dict:


              """simple docstring"""


              return ViTImageProcessor.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


              """simple docstring"""


              shutil.rmtree(self.tmpdirname      )







       def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[Any]:


              """simple docstring"""


              _a			  =       np.random.randint(2_55							,    size=(3, 30, 4_00)							,    dtype=np.uinta      )

              _a			  =       Image.fromarray(np.moveaxis(lowerCAmelCase_							,    0							,    -1      )      )

              return image_input







       def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


              """simple docstring"""


              _a			  =       self.get_tokenizer()
              _a			  =       self.get_image_processor()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )
              processor.save_pretrained(self.tmpdirname      )
              _a			  =       MgpstrProcessor.from_pretrained(self.tmpdirname							,    use_fast=lowerCAmelCase_      )

              self.assertEqual(processor.char_tokenizer.get_vocab()							,    tokenizer.get_vocab()      )
              self.assertIsInstance(processor.char_tokenizer							,    lowerCAmelCase_      )

              self.assertEqual(processor.image_processor.to_json_string()							,    image_processor.to_json_string()      )
              self.assertIsInstance(processor.image_processor							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[int]:


              """simple docstring"""


              _a			  =       self.get_tokenizer()
              _a			  =       self.get_image_processor()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )
              processor.save_pretrained(self.tmpdirname      )

              _a			  =       self.get_tokenizer(bos_token='''(BOS)'''							,    eos_token='''(EOS)'''      )
              _a			  =       self.get_image_processor(do_normalize=lowerCAmelCase_							,    padding_value=1.0      )

              _a			  =       MgpstrProcessor.from_pretrained(
                  self.tmpdirname							,    bos_token='''(BOS)'''							,    eos_token='''(EOS)'''							,    do_normalize=lowerCAmelCase_							,    padding_value=1.0      )

              self.assertEqual(processor.char_tokenizer.get_vocab()							,    tokenizer_add_kwargs.get_vocab()      )
              self.assertIsInstance(processor.char_tokenizer							,    lowerCAmelCase_      )

              self.assertEqual(processor.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()      )
              self.assertIsInstance(processor.image_processor							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[int]:


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       self.prepare_image_inputs()

              _a			  =       image_processor(lowerCAmelCase_							,    return_tensors='''np'''      )
              _a			  =       processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

              for key in input_image_proc.keys():
                     self.assertAlmostEqual(input_image_proc[key].sum()							,    input_processor[key].sum()							,    delta=1e-2      )







       def      __lowerCAmelCase    (						self :						List[Any]      )    ->							List[Any]:


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       '''test'''

              _a			  =       processor(text=lowerCAmelCase_      )

              _a			  =       tokenizer(lowerCAmelCase_      )
              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]							,    encoded_processor[key]      )







       def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       '''test'''
              _a			  =       self.prepare_image_inputs()

              _a			  =       processor(text=lowerCAmelCase_							,    images=lowerCAmelCase_      )

              self.assertListEqual(list(inputs.keys()      )							,    ['''pixel_values''', '''labels''']      )

              # test if it raises when no input is passed
              with pytest.raises(lowerCAmelCase_      ):
                     processor()







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


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

              _a			  =       processor.char_decode(lowerCAmelCase_      )
              _a			  =       tokenizer.batch_decode(lowerCAmelCase_      )
              _a			  =       [seq.replace(''' '''							,    ''''''      ) for seq in decoded_tok]

              self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       None
              _a			  =       self.prepare_image_inputs()

              _a			  =       processor(text=lowerCAmelCase_							,    images=lowerCAmelCase_      )

              self.assertListEqual(list(inputs.keys()      )							,    processor.model_input_names      )







       def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Tuple:


              """simple docstring"""


              _a			  =       self.get_image_processor()
              _a			  =       self.get_tokenizer()

              _a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

              _a			  =       torch.randn(1							,    27							,    38      )
              _a			  =       torch.randn(1							,    27							,    5_02_57      )
              _a			  =       torch.randn(1							,    27							,    3_05_22      )

              _a			  =       processor.batch_decode([char_input, bpe_input, wp_input]      )

              self.assertListEqual(list(results.keys()      )							,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']      )







'''simple docstring'''


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
				from transformers import LlamaTokenizerFast
except ImportError as e:
				warnings.warn(e)
				warnings.warn(
				    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
				)
				_snake_case   :   int     							=	None
_snake_case   :   Tuple     							=	{
    '7B': 11008,
    '13B': 13824,
    '30B': 17920,
    '65B': 22016,
    '70B': 28672,
}
_snake_case   :   int     							=	{
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[Any]=1				,		UpperCamelCase :						Optional[Any]=256   ):





							'''simple docstring'''


							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''r'''   ) as f:
														return json.load(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''w'''   ) as f:
														json.dump(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						str=True   ):





							'''simple docstring'''


							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )
							_a			  =       os.path.join(UpperCamelCase				,		'''tmp'''   )
							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )

							_a			  =       read_json(os.path.join(UpperCamelCase				,		'''params.json'''   )   )
							_a			  =       NUM_SHARDS[model_size]
							_a			  =       params['''n_layers''']
							_a			  =       params['''n_heads''']
							_a			  =       n_heads // num_shards
							_a			  =       params['''dim''']
							_a			  =       dim // n_heads
							_a			  =       10000.0
							_a			  =       1.0 / (base ** (torch.arange(0				,		UpperCamelCase				,		2   ).float() / dims_per_head))

							if "n_kv_heads" in params:
														_a			  =       params['''n_kv_heads''']  # for GQA / MQA
														_a			  =       n_heads_per_shard // num_key_value_heads
														_a			  =       dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														_a			  =       n_heads
														_a			  =       n_heads_per_shard
														_a			  =       dim

							# permute for sliced rotary
							def permute(UpperCamelCase :						List[str]				,		UpperCamelCase :						str=n_heads				,		UpperCamelCase :						int=dim				,		UpperCamelCase :						Optional[Any]=dim   ):
														return w.view(UpperCamelCase				,		dima // n_heads // 2				,		2				,		UpperCamelCase   ).transpose(1				,		2   ).reshape(UpperCamelCase				,		UpperCamelCase   )

							print(f'Fetching all parameters from the checkpoint at {input_base_path}.'   )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														_a			  =       torch.load(os.path.join(UpperCamelCase				,		'''consolidated.00.pth'''   )				,		map_location='''cpu'''   )
							else:
														# Sharded
														_a			  =       [
														    torch.load(os.path.join(UpperCamelCase				,		f'consolidated.{i:02d}.pth'   )				,		map_location='''cpu'''   )
														    for i in range(UpperCamelCase   )
														]
							_a			  =       0
							_a			  =       {'''weight_map''': {}}
							for layer_i in range(UpperCamelCase   ):
														_a			  =       f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
														if model_size == "7B":
																					# Unsharded
																					_a			  =       {
																					    f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wq.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wk.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
																					    f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
																					    f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
																					    f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
																					    f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					_a			  =       {
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.attention_norm.weight'
																					    ].clone(),
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.ffn_norm.weight'
																					    ].clone(),
																					}
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )   )
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
																					                UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		)
																					_a			  =       torch.cat(
																					    [
																					        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
																					            UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					        for i in range(UpperCamelCase   )
																					    ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )

																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )

														_a			  =       inv_freq
														for k, v in state_dict.items():
																					_a			  =       filename
																					param_count += v.numel()
														torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							_a			  =       f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
							if model_size == "7B":
														# Unsharded
														_a			  =       {
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														_a			  =       {
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(UpperCamelCase   )]				,		dim=1   ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(UpperCamelCase   )]				,		dim=0   ),
														}

							for k, v in state_dict.items():
														_a			  =       filename
														param_count += v.numel()
							torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							# Write configs
							_a			  =       {'''total_size''': param_count * 2}
							write_json(UpperCamelCase				,		os.path.join(UpperCamelCase				,		'''pytorch_model.bin.index.json'''   )   )
							_a			  =       params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							_a			  =       params['''multiple_of'''] if '''multiple_of''' in params else 256
							_a			  =       LlamaConfig(
							    hidden_size=UpperCamelCase				,		intermediate_size=compute_intermediate_size(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )				,		num_attention_heads=params['''n_heads''']				,		num_hidden_layers=params['''n_layers''']				,		rms_norm_eps=params['''norm_eps''']				,		num_key_value_heads=UpperCamelCase				,		)
							config.save_pretrained(UpperCamelCase   )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''   )
							_a			  =       LlamaForCausalLM.from_pretrained(UpperCamelCase				,		torch_dtype=torch.floataa				,		low_cpu_mem_usage=UpperCamelCase   )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''   )
							model.save_pretrained(UpperCamelCase				,		safe_serialization=UpperCamelCase   )
							shutil.rmtree(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							_a			  =       LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'   )
							_a			  =       tokenizer_class(UpperCamelCase   )
							tokenizer.save_pretrained(UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''				,		help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''				,		)
							parser.add_argument(
							    '''--model_size'''				,		choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']				,		)
							parser.add_argument(
							    '''--output_dir'''				,		help='''Location to write HF model and tokenizer'''				,		)
							parser.add_argument('''--safe_serialization'''				,		type=UpperCamelCase				,		help='''Whether or not to save using `safetensors`.'''   )
							_a			  =       parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir				,		input_base_path=os.path.join(args.input_dir				,		args.model_size   )				,		model_size=args.model_size				,		safe_serialization=args.safe_serialization				,		)
							_a			  =       os.path.join(args.input_dir				,		'''tokenizer.model'''   )
							write_tokenizer(args.output_dir				,		UpperCamelCase   )


if __name__ == "__main__":
				main()

